FRIDAY
JUNE 30

INFORMSTHURSDAYJUNE 29

21stBook of abstract
WEDNESDAY
JUNE 28

JUNE 28-30
2023
NAN
CY 2-02F3RANCEINFORMS-APS2023.EVENT.UNIV-LORRAINE.FR

Contents
Committees

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Sponsors

16

Program at a glance

17

List of invited session organizers

21

List of speaker24

1 Plenary talks
1.1 Frédéric Chazal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Beatrice Meini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3 Sylvie Méléard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4 Sean Meyn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 Amy Ward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Tutorial lectures
2.1 Marc Lelarge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Canceled : Sarah Penington . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Abstracts of invited talks and contributed talks
31
3.1 Wednesday 28th, 10:30 - 12:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.1.1 Invited session W1-1 (Baccelli & Foss): Mean-field approach for analysis of complex
stochastic systems I (Auditorium) . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
- Davydov Michel: Propagation of chaos for replica mean-field models of intensitybased neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
- Gast Nicolas: The bias of mean field approximation . . . . . . . . . . . . . . . . 31
- Ramanan Kavita: Limits of empirical measures of interacting jump processes onsparse graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
- Baccelli Francois & Foss Sergey: Migration-Contagion Processes . . . . . . . . . 32
3.1.2 Invited session W1-2 (Haviv): Strategic queueing I (Room 101) . . . . . . . . . . . 32
- Haviv Moshe: Intermediate priorities and the balancing of the C-mu rule . . . . . 32
- Hassin Refael: Optimal and Self Selection of Service Type in a Queueing System
where Long Service Postpones the Need for the Next Service . . . . . . . 32
- Clarkson Jake: On the Price of Information in Queueing . . . . . . . . . . . . . . 33
- Bountali Olga: Strategic Double-Booking and Its Impact on Healthcare Operations 33
3.1.3 Invited session W1-3 (Ibrahim): Scheduling in service systems (Room 102) . . . . 33
- Daw Andrew: Closing the Service: Service Success Heterogeneity and Its Impact
on Routing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
- Dong Jing: Optimal routing under demand surge: the value of future arrival rate
information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
- Scully Ziv: Performance of the Gittins Policy in the G/G/1 and G/G/k, Withand Without Setup Times . . . . . . . . . . . . . . . . . . . . . . . . . . 34
- Zhong Yueyang: Learning the Scheduling Policy in Time-Varying Multiclass Many
Server Queues with Abandonment . . . . . . . . . . . . . . . . . . . . . . 34
3.1.4 Invited session W1-4 (Champagnat & Villemonais): Quasi-stationary distributions
in numerical stochastic methods and statistics (Room 103) . . . . . . . . . . . . . 35
- Corujo Rodriguez Josué: Convergence of the empirical measure induced by aMoran type particle system . . . . . . . . . . . . . . . . . . . . . . . . . . 35
- Fritsch Coralie: Quasi-stationary behavior of the Crump-Young model of chemostat 35

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3.2

3- Strickler Edouard: Wasserstein contraction and convergence of penalized Markov
processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
- Zalduendo Vidal Nicolas: The multi-type bisexual Galton-Watson branching process 36
3.1.5 Invited session W1-5 (Robert): Stochastic Chemical Reaction Networks (Room 104) 36- Popovic Lea: Stationary distributions for reaction networks with discretenessinduced transitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
- Cappelletti Daniele: Two-dimensional weakly reversible reaction networks are
positive recurrent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
- Laurence Lucie: Scaling methods for stochastic chemical reaction networks. . . . 37
- Loeser Eva: Fluid Limit for a Stochastic Model of Enzymatic Processing with
General Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.1.6 Contributed session W1-6 (Harsha): Markov control and decision making (Room105) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
- Chehrazi Naveed: Dynamic Filtration and the Curse of Dimensionality . . . . . . 37
- Mouchtaki Omar: Beyond IID: Data-Driven Decision-Making in Heterogeneous
Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
- Neufeld Ariel: Markov Decision Processes under Model Uncertainty . . . . . . . 38
- Honnappa Harsha: Offline Estimation of Controlled Markov Chains: Minimaxity
and Sample Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.1.7 Invited session W1-7 (Ben Hamou): Random walks on random graphs (Room 106) 38- Nagy Oliver: Linking the mixing times of random walks on static and dynamic
random graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
- Stephan Ludovic: Tensor completion via nomadic walks . . . . . . . . . . . . . . 39
- Ben-Hamou Anna: Mixing time of the non-backtracking random walk on twocommunity random graphs . . . . . . . . . . . . . . . . . . . . . . . . . . 39
- Sarkovic Andjela: Cutoff for random walk on random graphs with a community
structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.1.8 Invited session W1-8 (Adan): Random walks and product forms (Room 202) . . . 39
- Boucherie Richard: Blocking policies for networks of finite capacity quasi-reversible
nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
- Dimitriou Ioannis: On some reflected autoregressive processes with dependencies 40- Walraevens Joris: A discrete-time random walk with jumps in the quarter plane
motivated by road traffic . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
- Adan Ivo: On the Generating Function Approach for the Analysis of Queueing
Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.1.9 Invited session W1-9 (Coculescu): Risk measures in finance and insurance (Room203) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
- Bignozzi Valeria: Fair valuation under parameter uncertainty . . . . . . . . . . . 41
- Grigorova Miryana: Optimal stopping: Bermudan strategies meet non-linear evaluations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
- Hillairet Caroline: Hawkes processes and application to cyber-insurance derivatives 41- Rosazza Gianin Emanuela: Capital allocation rules and generalized collapse to
the mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.1.10 Contributed session W1-10 (Lim): Simulation and Inference (Room 204) . . . . . 42
- Dwivedi Raaz: Counterfactual inference for sequential experiments . . . . . . . . 42
- Bayati Mohsen: Adaptive Experiment Design with Panel Data . . . . . . . . . . 42
- Lu Yingdong: An analytical approach to Hamiltonian Monte Carlo . . . . . . . . 42
- Lim Eunji: The limiting behavior of isotonic and convex regression estimators
when the model is misspecified . . . . . . . . . . . . . . . . . . . . . . . . 43
3.1.11 Contributed session W1-11 (Xie): Optimization, games (Room 205) . . . . . . . . 43
- Keppo Jussi: The Wisdom of Strategically Diverse Crowds . . . . . . . . . . . . 43
- Saha Subhamay: Partially Observable Discrete-time Discounted Markov Games
with General Utility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
- Pal Chandan: Discrete-time zero-sum games for Markov chains with risk-sensitive
average cost criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
- Xie Yao: Sinkhorn distributionally robust optimization with application to robust
hypothesis tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Wednesday 28th, 13:30 - 15:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.1 Invited session W2-2 (Zhan): Strategic queueing II (Room 101) . . . . . . . . . . 44
- Afèche Philipp: Pricing In A Two-sided Market With Time-sensitive Customers
And Suppliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
- Zhong Yueyang: Behavior-Aware Queueing: When Strategic Customers Meet
Strategic Servers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

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4- Zur (Schwarz) Elisheva: Priority queues with arrival rate uncertainty . . . . . . . 45
- Dongyuan Zhan: Advance Selling and Upgrading in Priority Queues . . . . . . . 45
3.2.2 Invited session W2-3 (Dong): Efficient scheduling and resource allocation (Room102) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
- Arlotto Alessandro: On the constant regret in dynamic resource allocation problems 45- Ibrahim Rouba: Size-based scheduling in service systems . . . . . . . . . . . . . . 45
- Sarhangian Vahid: Dynamic transfer policies for parallel queues . . . . . . . . . . 46
- Shi Pengyi: Refined Mean-Field Approximation for Discrete-Time Queues to
Improve Itinerary Completion in Coordinated Care Networks . . . . . . . 46
3.2.3 Invited session W2-4 (Fricker): Mean-field approach for analysis of complex stochastic systems II (Room 103) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
- Allmeier Sebastian: Bias of Multiscale Mean Field Models and Averaging Methods 46- Castiel Eyal: Time scale separation in Heavy traffic for Queue Based CSMA . . . 47
- El Masmari Soukaina: The asymptotic behavior of a large-scale storage systemwith loss, duplication and admitting policies . . . . . . . . . . . . . . . . 47
- Mohamed Hanène: Large scale analysis of a stochastic model for a system with
two types of particles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.4 Invited session W2-5 (Fraiman): Algorithms on random graphs (Room 104) . . . . 48
- Briend Simon: Finding adam under polynomial connectivity assumption . . . . . 48
- Perarnau Guillem: Short Synchronizing Words for Random Automata . . . . . . 48
- Skerman Fiona: Is it easier to count communities than find them? . . . . . . . . 48
- Stegehuis Clara: Is this network problem hard? Theory vs practice . . . . . . . . 48
3.2.5 Invited session W2-6 (Namkoong): Frontiers in sequential learning (Room 105) . . 49
- Caldentey Rene: Diffusion Approximations for a Class of Sequential Experimentation Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
- Kalvit Anand: Diffusion limits of multi-armed bandit experiments under optimismbased policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
- Zhang Kelly: Inference after Using RL to Learn Across Users for Digital Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
- Namkoong Hongseok: Adaptive Experimentation at Scale . . . . . . . . . . . . . 50
3.2.6 Invited session W2-7 (Litvak): Spatial network models (Room 106) . . . . . . . . 50
- Bindiganavile Ramadas Vinay Kumar: Community detection on block models
with geometric kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
- Dreveton Maximilien: Higher-Order Spectral Clustering for Geometric Graphs . 50
- Van Der Hoorn Pim: Convergence of discrete graph curvature for random geometric graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
- Weedage Lotte: Resource and location sharing in spatial networks . . . . . . . . 51
3.2.7 Invited session W2-8 (Weiss): Applications of Dynamic Matching (Room 202) . . 51
- Chen Yilun: Bridging online and offline matching: Simulate-Optimize-AssignRepeat (SOAR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
- Kohlenberg Angela: The Cost of Impatience in Dynamic Matching: Scaling Laws
and Operating Regimes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
- Qian Pengyu: The Competition for Partners in Matching Markets . . . . . . . . 52
- Weiss Gideon: On A Conjecture for FCFS Parallel Service Systems . . . . . . . . 52
3.2.8 Invited session W2-9 (Loisel): Actuarial mathematics and quantitative risk management (Room 203) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
- Barigou Karim: Gaussian Process-Based Mortality Monitoring using Multivariate
Cumulative Sum Procedures . . . . . . . . . . . . . . . . . . . . . . . . . 52
- Toyoizumi Hiroshi: Optimal Dividend Strategy with Poisson Jump Revenue andConstant Negative Running Cost . . . . . . . . . . . . . . . . . . . . . . . 53
- Marek Oheim: Risk Class Management in Life Insurance Markets . . . . . . . . . 53
- Loisel Stéphane: Quickest detection problem and actuarial applications . . . . . 53
3.2.9 Invited session W2-10 (Mijatovic): Simulation, Estimation and Monte Carlo Algorithms for Levy Processes (Room 204) . . . . . . . . . . . . . . . . . . . . . . . . . 53
- Mariucci Ester: Nonparametric density estimation of small jumps of Lévy processes 53- Gonzalez Cazares Jorge: Simulation of the drawdown and its duration in Lévy
models via stick-breaking Gaussian approximation . . . . . . . . . . . . . 54
- Lin Feng: Fast exact simulation of the first passage of a tempered stable subordinator 54- Mijatovic Aleksandar: Geometrically Convergent Simulation of the Extrema of
Lévy Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2.10 Contributed session W2-11 (Vallois): Health and healthcare modeling (Room 205) 54- Wang Zicheng: Clonal Diversity at Cancer Recurrence . . . . . . . . . . . . . . . 54
- Jacko Peter: Bandit Procedures for Designing Patient-Centric Clinical Trials . . 55

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3.3

5- De Turck Koen: Limits of arrival processes into vaccination centers with unpunctual patients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
- Vallois Pierre: Probabilistic Cellular Automata modeling of intercellular interactions in airways: complex pattern formation in patients with Chronic
Obstructive Pulmonary Disease . . . . . . . . . . . . . . . . . . . . . . . 55
Wednesday 28th, 15:30 - 17:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3.1 Invited session W3-1 (Dorsman, Righter & Gardner): Load balancing I (Auditorium) 56- Comte Céline: Reinforcement Learning in Product-Form Queueing Networks . . 56
- Cardinaels Ellen: Heavy-Traffic Analysis of Redundancy Systems without Complete Resource Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
- Anton Elene: Novel coupling techniques for the stationary distribution of redundancyd models with general scheduling policies . . . . . . . . . . . . . . . . . . 57
- Gardner Kristen: New directions in product forms . . . . . . . . . . . . . . . . . 57
3.3.2 Invited session W3-2 (Burnetas): Strategic queueing III (Room 101) . . . . . . . . 57
- Burnetas Apostolos: Price and Capacity Competition between a Make-to-Orderand a Make-to-Stock Firm with Strategic Customers . . . . . . . . . . . . 57
- Liberopoulos George: Price and Capacity Optimization in Service Systems with
Time-Sensitive, Mixed-Risk Customers . . . . . . . . . . . . . . . . . . . 58
- Oz Binyamin: Strategic flexibility in service systems . . . . . . . . . . . . . . . . 58
- Manou Athanasia: Heterogeneous Strategic Customers in an M/M/1 queue: Priceof Ignoring Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.3 Invited session W3-3 (Maguluri): Stochastic processing networks (Room 102) . . . 58
- Borst Sem: Load Balancing with Sparse Dynamic Graphs . . . . . . . . . . . . . 58
- Dai Jim: Asymptotic steady-state independence for generalized Jackson networks
in multi-scale heavy traffic . . . . . . . . . . . . . . . . . . . . . . . . . . 59
- Jhunjhunwala Prakirt: Heavy Traffic Joint Queue Length Distribution without
Resource Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
- Varma Sushil: Power-of-d Choices Load Balancing in the Sub-Halfin Whitt Regime 59
3.3.4 Invited session W3-4 (Cohen): Mean-Field Limits and their Applications (Room103) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
- Kobeissi Ziad: The tragedy of the commons: a Mean-Field Game approach to thereversal of travelling waves . . . . . . . . . . . . . . . . . . . . . . . . . . 60
- Lauriere Mathieu: Deep Backward and Galerkin Methods for Learning Finite
State Master Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
- Yeung Lane Chun: A non-asymptotic perspective on mean field control via logconcavity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
- Cohen Asaf: Markovian Equilibria In Ergodic Many-Player Games and MeanField Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.5 Invited session W3-5 (Olvera-Cravioto): Processes on graphs (Room 104) . . . . . 61
- Britton Tom: Semi-directed networks and their relation to epidemic models . . . 61
- Diallo Aoudi Mohamed Habib Aliou: Large graph limit of the matching coverageof local online algorithms on the Configuration model . . . . . . . . . . . 61
- Ramanan Kavita: Large deviations for interacting diffusions on local tree-likegraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
- Olvera-Cravioto Mariana: Opinion dynamics on directed complex networks . . . 61
3.3.6 Invited session W3-6 (Xu): Experimentation and Learning with Stochastic Models(Room 105) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
- Agarwal Anish: On Causal Inference with Temporal and Spatial Spillovers inPanel Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
- Li Hannah: Scaling Up Healthcare Interventions: Insights from Experimentation
62- Li Shuangning: Experimenting under Stochastic Congestion . . . . . . . . . . . . 62
- Xu Kuang: Service Slowdown Detection . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.7 Invited session W3-7 (Goulart): Random matrices and random tensors in data
analysis and machine learning (Room 106) . . . . . . . . . . . . . . . . . . . . . . 63
- Lebeau Hugo: Truncated HOSVD : An Analysis of a Fast Tensor Approximation 63- Mai Xiaoyi: A high dimensional asymptotic view on learning from large data sets 63- Seddik Mohamed El Amine: Asymptotics of Asymmetric Random Tensors . . . . 63
- Zhu Yizhe: Overparameterized random feature regression with nearly orthogonal
data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3.8 Invited session W3-8 (Banerjee): Online decision making (Room 202) . . . . . . . 64
- Braun Alexander: Simplified Prophet Inequalities for Combinatorial Auctions . . 64
- Lomys Niccolò: A Mediator Approach to Mechanism Design with Limited Commitment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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3.4

6- Maggiori Andreas: Online Correlation Clustering . . . . . . . . . . . . . . . . . . 64
- Lykouris Thodoris: Group fairness in dynamic refugee assignment . . . . . . . . 65
3.3.9 Invited session W3-9 (Daw & Yom-Tov): Hawkes Processes: New Models andTheory (Room 203) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
- Chen Xinyun: Diffusion limit and staffing of many-server queues with Hawkes input 65- Daw Andrew: Conditional Uniformity and Hawkes Processes . . . . . . . . . . . 65
- Mandjes Michel: Infinite-server queues with Hawkes input . . . . . . . . . . . . . 66
- Weber Thomas: Estimation of Self-Exciting Point Processes from Time-Censored
Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.3.10 Invited session W3-10 (Brown): Advances in Experimentation and Dynamic Models(Room 204) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
- Haugh Martin: Counterfactual Analysis in Dynamic Latent State Models . . . . 66
- Miao Sentao: Adaptive Lagrangian Policies for Multi-Warehouse Multi-Store Inventory System with Lost Sales . . . . . . . . . . . . . . . . . . . . . . . . 67
- Zoumpoulis Spyros: A Sample Size Calculation for Training and Certifying Targeting Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
- Brown David: Sequential Search with Acquisition Uncertainty . . . . . . . . . . . 67
3.3.11 Contributed session W3-11 (Garcia): Stochastic analysis and estimation (Room 205) 68- Gerencsér Balázs: Convergence and cutoff analysis for a class of random walks
inspired by almost exchangeable data . . . . . . . . . . . . . . . . . . . . 68
- Levering Nikki: Dynamic Routing in a Road Traffic Network . . . . . . . . . . . 68
- Garcia Pareja Celia: Unbiased estimation of Wright-Fisher diffusions: an exact
simulation approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Thursday 29th, 10:30 - 12:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.4.1 Invited session T1-1 (Avrachenkov & Ayesta): Reinforcement learning (Auditorium) 69- Borkar Vivek: Q-learning in unknown environments . . . . . . . . . . . . . . . . 69
- Gaujal Bruno: The Sliding Regret or How to Control Bad Episodes in Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
- Robledo Francisco: A Two-Timescale Approach for Learning Whittle Index in
RMABPs: Tabular and Neural Network Implementations . . . . . . . . . 69
- Srikant R: Performance Bounds for Policy-Based Average Reward Reinforcement
Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.4.2 Invited session T1-2 (Hasenbein): Strategic queueing IV (Room 101) . . . . . . . . 70
- Snitkovsky Ran: Stochastic Approximation of Symmetric Nash Equilibria in
Queueing Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
- Van Eekelen Wouter: Rational customers with limited information joining nonobservable queues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
- Logothetis Dimitrios: Reneging behavior of strategic customers in a system withan unreliable server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
- Dimitrakopoulos Yiannis: The impact of subcontracting practice on Markovian
queues with strategic customers . . . . . . . . . . . . . . . . . . . . . . . 71
3.4.3 Invited session T1-3 (Castellanos): Queueing Systems with Predictions (Room 102) 71- Hu Yue: Prediction-Driven Surge Planning with Application in the Emergency
Department . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
- Singh Simrita: Feature-Based Priority Queuing . . . . . . . . . . . . . . . . . . . 71
- Yom-Tov Galit: Optimising Hematology Patients LOS Using Mortality and Infection Risk Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
- Legros Benjamin: Why rational queueing of loss-averse users undermines thebenefits of added resources . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.4.4 Invited session T1-4 (Baccelli & Foss): Infinite-dimensional queueing/communication
systems (Room 103) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
- Curien Nicolas: Parking problems on random trees I . . . . . . . . . . . . . . . . 72
- Curien Nicolas: Parking problems on random trees II . . . . . . . . . . . . . . . . 73
- Sankararaman Abishek: Interference Queueing Networks on Grids . . . . . . . . 73
- Wang Zhe: The Poisson HailModel, Stability, and Power Law Conditions . . . . 73
3.4.5 Invited session T1-5 (Coron): Stochastic models for ecology and evolution (Room104) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
- Costa Manon: Eco-evolutionary cycles in a multi-type predator-prey system . . . 73
- Marguet Aline: Spread of parasites affecting death and division rates in a cell
population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
- Pfaffelhuber Peter: Population genetic models for transposable elements . . . . . 74
- Tourniaire Julie: A branching particle system as a model of pushed fronts . . . . 74

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3.4.6

3.5

7

Invited session T1-6 (Shi): Learning algorithms for managing service systems (Room105) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
- Bastani Hamsa: Decision-Aware Learning for Global Health Supply Chains . . . 74
- Che Ethan: Stochastic Gradient Descent with Adaptive Data: Applications toOnline Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
- Chen Xinyun: Data Pooling for Personalized Intervention via Contextual Bandits 75- Kumar Rachitesh: Online Resource Allocation under Horizon Uncertainty . . . . 75
3.4.7 Invited session T1-7 (Olvera-Cravioto): Graph dynamics (Room 106) . . . . . . . 76
- Bhamidi Shankar: Dynamic networks: one has to do the "math" . . . . . . . . . 76
- Contat Alice: Critical core percolation on random graphs . . . . . . . . . . . . . 76
- Ray Rounak: Universality of the local limit of preferential attachment models . . 76
- Wang Tiandong: Random Networks with Heterogeneous Reciprocity . . . . . . . 76
3.4.8 Invited session T1-8 (Dorsman, Righter & Gardner): Load balancing II (Room 202) 77- Mukherjee Debankur: Load Balancing with Data Locality: Extending Mean-Field
Framework to Constrained Heterogeneous Systems . . . . . . . . . . . . . 77
- Jhunjhunwala Prakirt: Join-the-Shortest Queue with Abandonment: Critically
Loaded and Heavily Overloaded Regimes . . . . . . . . . . . . . . . . . . 77
- Zubeldia Martin: Delayed Load Balancing . . . . . . . . . . . . . . . . . . . . . . 77
- Doroudi Sherwin: Queue-Length-Aware Dispatching in Large-Scale Heterogeneous
Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.4.9 Invited session T1-10 (Torrisi): Topics on point processes models : theory andapplications (Room 204) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
- Flint Ian: An Equilibrium Spatial Model of Crime and Surveillance . . . . . . . . 78
- Kirchner Matthias: Critical cluster cascades . . . . . . . . . . . . . . . . . . . . . 78
- Privault Nicolas: Closed-form computation of the Hawkes cumulants with exponential kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78
Thursday 29th, 13:30 - 15:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.5.1 Invited session T2-1 (Ramanan): Measure-valued process limits of stochastic networks (Auditorium) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
- Atar Rami: Many-server queues, invariant states, and entropy . . . . . . . . . . . 79
- Maillard Pascal: Algorithmic hardness thresholds for the continuous random energy model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
- Véber Amandine: A multitype growth-fragmentation process to model the growthof a filamentous fungus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
- Yasodharan Sarath: Large deviations of interacting jump processes on locallytree-like graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
3.5.2 Invited session T2-2 (Kanavetas): Strategic queueing V (Room 101) . . . . . . . . 80
- Kanavetas Odysseas: The effect of reneging in a fluid queue with alternating
service process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
- Economou Antonis: The impact of informing strategic customers about othercustomers decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
- Baron Opher: Data Driven Causal Modeling of Queueing Systems . . . . . . . . 80
- Kerner Yoav: The Value of Service-Age Information in an Observable M/G/1
Queue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
3.5.3 Invited session T2-3 (Buke): Many-Server Queues (Room 102) . . . . . . . . . . . 81
- Bekker Rene: Multitasking Service Systems with Many Servers . . . . . . . . . . 81
- Mukhopadhyay Arpan: Performance of Large Multi-Server Systems with Parallelisable Jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
- Zhong Yueyang: Finite-Buffer Queues with Many Strategic Servers . . . . . . . . 82
- Buke Burak: Many-Server Systems with Heterogeneous and Strategic Servers . . 82
3.5.4 Invited session T2-4 (Nazarathy): Applied Probability for COVID-19 (Room 103) 82- Asanjarani Azam: Emulation of epidemics via Bluetooth-based virtual safe virus
spread . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
- Britton Tom: Disentangling the most effective behavioural changes to reducetransmission during the Covid-19 pandemic . . . . . . . . . . . . . . . . . 82
- Juneja Sandeep: Agent based simulators for epidemic modelling: Simulating larger
models using smaller ones . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
- Litvak Nelly: The role of inter-regional mobility in forecasting COVID-19 transimission in the Netherlands . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.5.5 Invited session T2-5 (Henry): Stochastic geometry (Room 104) . . . . . . . . . . . 83
- Blaszczyszyn Bartlomiej: Continuum Line-of-Sight Percolation on Poisson-Voronoi
Tessellations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

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3.6

8- Coupier David: Percolation on Delaunay triangulation to model long-range connection in 5G networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
- Heydenreich Markus: Phase transition of geometric inhomogeneous percolation
models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
- Jahnel Benedikt: Subcritical percolation phases for generalized weight-dependent
random connection models . . . . . . . . . . . . . . . . . . . . . . . . . . 84
3.5.6 Invited session T2-6 (Sanders): Stochastic learning in structured systems (Room105) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
- Goldsztajn Diego: Utility maximizing load balancing policies . . . . . . . . . . . 85
- Lee Junghyun: Nearly Optimal Latent State Decoding in Block MDPs . . . . . . 85
- Senen-Cerda Albert: Asymptotic convergence rate of Dropout in neural networkmodels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
- Van Werde Alexander: Detection and evaluation of clusters within sequential data 85
3.5.7 Invited session T2-7 (Avrachenkov): Network inference I (Room 106) . . . . . . . 86
- Gösgens Martijn: The Hyperspherical Geometry of Community Detection . . . . 86
- Leskelä Lasse: Community recovery from non-binary and higher-order network
interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
- Matias Catherine: Model-based clustering in simple hypergraphs through a stochastic blockmodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
- Xu Jiaming: Random Graph Matching in Geometric Models . . . . . . . . . . . . 87
3.5.8 Invited session T2-8 (Dorsman, Righter & Gardner): Load balancing III (Room 202) 87- Wang Weina: The M/M/k with Deterministic Setup Times . . . . . . . . . . . . 87
- Walton Neil: Stability and Optimization of Speculative Queueing Networks . . . 87
- Perry Ohad: Stability of parallel server systems . . . . . . . . . . . . . . . . . . . 88
- Hyytiä Esa: Are highly scalable sequential dispatching policies asymptotically
optimal? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.5.9 Invited session T2-10 (Decreusefond): Stein method I (Room 204) . . . . . . . . . 88
- Coutin Laure: Diffusive limits of Lipschitz functionals of Poisson measures . . . . 88
- Nourdin Ivan: Total variation bound for Hadwiger’s functional using Stein’s method 89- Reveillac Anthony: The Malliavin-Stein method for Hawkes functionals . . . . . 89
- Vuong Christophe: Malliavin-Stein method for conditionally independent randomvariables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Thursday 29th, 15:30 - 17:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
3.6.1 Invited session T3-1 (Blanchet): Advances in Theory and Algorithms for Decision
Making Under Uncertainty (Auditorium) . . . . . . . . . . . . . . . . . . . . . . . 89
- Dong Jing: A primal-dual approach to constrained Markov decision processes
with applications to queue scheduling and inventory management . . . . 89
- Qu Yanlin: Contractive Drift Conditions for Rates of Convergence of Markov
Chains in Wasserstein Distance . . . . . . . . . . . . . . . . . . . . . . . . 90
- Ghosh Soumyadip: Distributionally Robust Optimization of Boosting for Efficient
Ensemble Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
- Huang Zhiyuan: Conformal Data-Driven Chance Constraints . . . . . . . . . . . 90
3.6.2 Invited session T3-2 (Koole): Call centers (Room 101) . . . . . . . . . . . . . . . . 90
- Garlati Elena: Fractional staffing levels in Erlang models . . . . . . . . . . . . . . 90
- Li Siqiao: Data-driven prediction of service level for call centers . . . . . . . . . . 91
- Özümerzifon Ömer: Analysis of time-dependent queues with retrials: Impact of
retrial distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
- Ger Koole: The validation of call center workforce staffing models . . . . . . . . . 91
3.6.3 Invited session T3-3 (Ayhan): Control of Queueing Systems (Room 102) . . . . . . 91
- Lewis Mark: A stochastic decision-making scenario in Telemedicine . . . . . . . . 91
- Rebuffi Louis-Sébastien: Reinforcement Learning in a Birth and Death Process:
Breaking the Dependence on the State Space . . . . . . . . . . . . . . . . 92
- Righter Rhonda: Policy-Space Collapse in Matching Models . . . . . . . . . . . . 92
- Ayhan Hayriye: Optimal Pricing and Information Sharing in Queueing Systems . 92
3.6.4 Invited session T3-4 (Tran): Contact-tracing (Room 103) . . . . . . . . . . . . . . 92
- Vo Thi Phuong Thuy: Exploration of SBM graphs by the Respondent Driven
Sampling methods. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
- Zhang Dongni: Epidemic models with manual and digital contact tracing . . . . 93
- Stegehuis Clara: Contact tracing and network structures . . . . . . . . . . . . . . 93
- Doroudi Sherwin: Evaluating Disease Transmission Risks in Small-Scale Settings
Under the Possibility of Multiple Infectors . . . . . . . . . . . . . . . . . 94
3.6.5 Invited session T3-5 (Penington): Branching processes and their applications (Room104) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

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3.7

9- Gonzalez Casanova Adrian: The speed of (a variant of) Muller’s ratchet can becalculated using multi-type logistic branching processes . . . . . . . . . . 94
- Hartung Lisa: Systems of F-KPP equations . . . . . . . . . . . . . . . . . . . . . 94
- Pain Michel: The height of weighted recursive trees . . . . . . . . . . . . . . . . . 95
- Villemonais Denis: Some asymptotic results for discrete time weighted branchingparticle systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.6.6 Invited session T3-6 (Wang): Queues, bandits, and reinforcement learning (Room105) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
- Agrawal Shubhada: CRIMED: Lower and Upper Bounds on Regret for Bandits
with Unbounded Stochastic Corruption . . . . . . . . . . . . . . . . . . . 95
- You Wei: Information-Directed Selection for Top-Two Algorithms . . . . . . . . . 95
3.6.7 Invited session T3-7 (Backhausz): Random graphs and matrices (Room 106) . . . 96
- Holmgren Cecilia: The asymptotic distribution of cluster sizes for supercriticalpercolation on random split trees . . . . . . . . . . . . . . . . . . . . . . . 96
- Mathien Joffrey: Diameter of random surfaces built from random graphs. . . . . 96
- Müller Noela: The rank of sparse symmetric matrices over arbitrary fields . . . . 96
- Shneer Seva: First passage percolation on Erdos-Renyi graphs with general weights 96
3.6.8 Invited session T3-8 (Comte): Online stochastic matching: a diversity of viewpoints(Room 202) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
- Jonckheere Matthieu: Online matching for the multitype stochastic block model97- Kerimov Süleyman: Near-Optimal Policies for Dynamic Matching . . . . . . . . . 97
- Reiffenhäuser Rebecca: Online Max-Weight Matchings with Limited Priors . . . 97
- Zubeldia Martin: Matching Queues with Abandonments in Quantum Switches:
Stability and Throughput Analysis . . . . . . . . . . . . . . . . . . . . . . 97
3.6.9 Invited session T3-10 (Decreusefond): Stein method II (Room 204) . . . . . . . . . 98
- Halconruy Hélène: Stochastic analysis for marked binomial processes and Poisson
approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
- Peccati Giovanni: Stein’s method and quantitative two-scale stabilization . . . . 98
- Swan Yvik: Normal approximation for the posterior in exponential families . . . 98
- Decreusefond Laurent: Stein’s method for discrete alpha stable point processes . 99
Friday 30th, 10:30 - 12:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.7.1 Invited session F1-1 (Robert): Stochastic Models of Biology (Auditorium) . . . . . 99
- Zaherddine Jana: Stochastic Models of Regulation of Gene Expression in Biological Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
- Vignoud Gaetan: Spike-timing dependent plasticity in stochastic neuronal networks 99- Véber Amandine: Growth properties of the infinite-parent spatial Lambda-FlemingViot process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
- Campillo Fabien: Nonlinear filtering in neurosciences . . . . . . . . . . . . . . . . 100
3.7.2 Invited session F1-2 (Stolletz): Stochastic modeling of manufacturing and service
operations (Room 101) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100- Karabag Oktay: Production and Energy Mode Control of a Production-Inventory
System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100- Khayyati Siamak: A Hybrid Approximation Method for Time-Dependent Queues
in Service Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100- Schwarz Justus Arne: A production planning model for non-stationary and stochastic yield in the semiconductor industry . . . . . . . . . . . . . . . . . . . 101- Rochlin Igor: The Impact of Outdated Information and the Optimal Up- date
Policy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.7.3 Invited session F1-3 (Henderson): Healthcare (Room 102) . . . . . . . . . . . . . . 101- Chick Stephen: Adaptive Sequential Clinical Trials for Precision Medicine with
Predictive and Prognostic Covariates . . . . . . . . . . . . . . . . . . . . 101- Van Der Mei Rob: Reducing waiting times by coordination in complex acutehealthcare systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101- Henderson Shane: Community First Responders: Recruitment Guidelines andDispatch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.7.4 Contributed session F1-4 (Wang): Networks and optimization (Room 103) . . . . 102- Lim Andrew: Mechanisms for Coordinating Systems of Decentralized Agents . . 102- Avramidis Thanos: Dynamic Pricing with Finite Price Sets via Upper Confidence
Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102- Jiang Jiashuo: Degeneracy is OK: Logarithmic Regret for Network Revenue Management with Indiscrete Distributions . . . . . . . . . . . . . . . . . . . . 103- Wang Jinting: Value-at-Risk Based Queueing . . . . . . . . . . . . . . . . . . . . 103

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3.7.5

3.8

10

Invited session F1-5 (De Saporta): Learning while controling: crossroads between
stochastic control and statistical learning (Room 104) . . . . . . . . . . . . . . . . 103- Le Quellennec Orlane: An example of medical treatment optimization undermodel uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103- Maillard Odalric-Ambrym: Regret minimization in MDPs: Lower bound-guided
strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104- Pham Huyên: Policy gradient learning methods for stochastic control with exit
time and applications to share repurchase pricing . . . . . . . . . . . . . 104- Trottner Lukas: Learning to reflect - Data-driven solutions to singular control
problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.7.6 Invited session F1-6 (Jonckheere): Probabilistic tools for learning (Room 105) . . 105- Ferragut Andres: Spatial estimation of EV energy demand based on aggregatedmeasurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105- Jonckheere Matthieu: Metric Learning : Choosing the parameter of the Fermat
distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105- Mastropietro Daniel: Fleming-Viot particle systems as a mechanism to improvelearning from rare events . . . . . . . . . . . . . . . . . . . . . . . . . . . 105- Keppo Jussi: Diversified Learning: Bayesian Control with Multiple Biased Information Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
3.7.7 Invited session F1-7 (Hachem): Large random matrices and statistical learning(Room 106) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106- Fan Zhou: Random linear estimation with rotationally-invariant designs: Asymptotics at high temperature . . . . . . . . . . . . . . . . . . . . . . . . . . 106- Loubaton Philippe: Testing that a large number of time series are independent
using linear spectral statistics of the estimated spectral coherence matrix 106- Maillard Antoine: Some advances on extensive-rank matrix denoising and factorization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107- Stephan Ludovic: Sparse random hypergraphs: Non-backtracking spectra andcommunity detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
3.7.8 Invited session F1-8 (Perchet): Online and dynamic matchings (Room 202) . . . . 107- Aouad Ali: A Nonparametric Framework for Online Stochastic Matching with
Correlated Arrivals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107- Lykouris Thodoris: Efficient Decentralized Multi-Agent Learning in Asymmetric
Bipartite Queueing Systems . . . . . . . . . . . . . . . . . . . . . . . . . 108- Sentenac Flore: Online Matching in Bipartite Random Graphs . . . . . . . . . . 108- Tercieux Olivier: Unpaired Kidney Exchange: Overcoming Double Coincidenceof Wants without Money . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
3.7.9 Invited session F1-9 (Kazi-Tani): Recent advances in actuarial science (Room 203) 109- Ecoto Geoffrey: Forecasting the cost of drought events in France by super learning 109- Goffard Pierre-Olivier: Stochastic models for blockchain analysis . . . . . . . . . 109- Weber Stefan: Modeling and Pricing Cyber Insurance . . . . . . . . . . . . . . . 109- Kazi-Tani Nabil: Stochastic differential Colonel Blotto games on graphs - Applications to cyber security . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
3.7.10 Invited session F1-10 (Murthy): Advances in stochastic modelling for decisionmaking under uncertainty (Room 204) . . . . . . . . . . . . . . . . . . . . . . . . . 110- Feng Yifan: Nested Elimination: A Simple Algorithm for Best-Item Identificationfrom Choice-Based Feedback . . . . . . . . . . . . . . . . . . . . . . . . . 110- Deo Anand: Risk Averse Decision Making Using Tail Self-Similarity . . . . . . . 110- Das Bikramjit: Aggregating heavy-tails: tracking large jumps from random sumsto Lévy processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Friday 30th, 13:30 - 15:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
3.8.1 Invited session F2-2 (Ravner): Statistical inference for queueing systems (Room 101)111- Bodas Shreehari: Parametric inference of the periodic arrival rate to a queue withunobserved balking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111- Bodas Tejas: Some recent results on the convert queueing problem . . . . . . . . 111- Wichelhaus Cornelia: Nonparametric approaches for unreliable queueing systems 111- Ravner Liron: Nonparametric estimation of the M/G/1 job-size distribution fromdiscrete workload observations . . . . . . . . . . . . . . . . . . . . . . . . 112
3.8.2 Invited session F2-3 (Hu): Queueing theory and its applications (Room 102) . . . 112- Ata Baris: Dynamic Control of MultiClass Queue in Halfin-Whitt Regime: A
Computational Approach for High-Dimensional problems . . . . . . . . . 112- Castellanos Antonio: An Infinite Server Queueing Model for Incarceration Diversion Decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

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3.9

11- Hu Yue: Optimal Scheduling of Proactive Service with Customer Deterioration
and Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112- Daw Andrew: Asymmetries of Service: Interdependence and Synchronicity . . . . 113
3.8.3 Invited session F2-5 (Bhulai): Markov decision problems (Room 104) . . . . . . . 113- Koopmans Camiel: Optimal service control and observation restoration decisionsin Markovian queues with fallible sensors. . . . . . . . . . . . . . . . . . . 113- Singh Vikas Vikram: Joint chance-constrained Markov decision Processes . . . . 113- Spieksma Floske: The deviation matrix of a denumerable, multichain Markov
process in continuous time . . . . . . . . . . . . . . . . . . . . . . . . . . 114- Bhulai Sandjai: Optimal stopping policies when assessing students’ mastery . . . 114
3.8.4 Contributed session F2-6 (Hautphenne): Advanced stochastic models (Room 105) 114- Khezeli Ali: An Improved Lower Bound on the Largest Common Subtree ofRandom Leaf-Labeled Binary Trees . . . . . . . . . . . . . . . . . . . . . 114- Ganguly Ankan: Hydrodynamic Limits of Interacting Particle Systems on Sparse
Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114- Mertz Laurent: A Piecewise Deterministic Markov Process approach modeling a
dry friction problem with noise . . . . . . . . . . . . . . . . . . . . . . . . 115- Hautphenne Sophie: Consistent estimation for population-size-dependent branching processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
3.8.5 Invited session F2-7 (Leskela): Network inference II (Room 106) . . . . . . . . . . 115- Deka Prabhanka: PageRank Nibble on the sparse directed stochastic block model 116- Lelarge Marc: Graph matching: from fundamental limits to algorithms . . . . . . 116- Sanders Jaron: Spectral norm bounds for, and singular value distributions of,block Markov chains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116- Zhang Anderson Ye: Fundamental Limits of Spectral Clustering in Stochastic
Block Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
3.8.6 Invited session F2-8 (Busic): Stochastic matching and applications (Room 202) . . 116- Banerjee Siddhartha: Priorities and Quotas in Online Matching . . . . . . . . . . 117- Comte Céline: Stochastic Dynamic Matching: A Mixed Graph-Theory and LinearAlgebra Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117- Fourneau Jean-Michel: Performance Paradox of Dynamic Matching under Greedy
Policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117- Moyal Pascal: Stability regions of systems with compatibilities, and ubiquitous
measures on graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
3.8.7 Invited session F2-9 (Carassus): Robust finance (Room 203) . . . . . . . . . . . . 118- Denis Laurent: Robust maximization of recursive utility with penalization on themodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118- Pergamenchtchikov Serguei: Robust portfolio optimisation for jump-diffusion market models with transaction costs . . . . . . . . . . . . . . . . . . . . . . 118- Wiesel Johannes: The Uniform Diversification Strategy Is Optimal for Expected
Utility Maximization under High Model Ambiguity . . . . . . . . . . . . 118- Carassus Laurence: Quasi-sure essential supremum and applications to finance . 119
3.8.8 Invited session F2-10 (Rhee): Rare Events: Large Deviations, Simulation, and AI(Room 204) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119- Murthy Karthyek: Letting the samples speak: A new approach towards efficient
Importance Sampling for tail events . . . . . . . . . . . . . . . . . . . . . 119- Wang Xingyu: Eliminating Sharp Minima from SGD with Truncated Heavy-tailed
Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119- Zhe Su: Sample path large deviations for Levy processes and random walks with
log-normal increments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120- Rhee Chang-Han: From heavy-tailed large deviations to local stability analysis
to global dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
Friday 30th, 15:30 - 17:00 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
3.9.1 Invited session F3-1 (Lelarge): Graph matching and planted models in randomgraphs (Auditorium) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120- Araya Valdivia Ernesto: Seeded graph matching under the Correlated Gaussian
Wigner Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121- Ganassali Luca: Aligning graphs via detecting correlation in trees . . . . . . . . . 121- Mao Cheng: Detection-Recovery Gap for Planted Dense Cycles . . . . . . . . . . 121- Yu Sophie: Random graph matching at Otter’s tree-counting threshold via counting chandeliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
3.9.2 Invited session F3-2 (Huang & Xie): Stochastic models and their application (Room101) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

CONTENTS

12- Gao Xuefeng: Asymptotically Optimal Control of Make-to-Stock Systems . . . . 122- Dong Jing: Scheduling queues with different and multiple resource requirements . 122- Shi Pengyi: Inpatient Overflow Management With Proximal Policy Optimization 122- Wang Zhengli: A Stochastic Control Framework for New Venture Creation . . . 123
3.9.3 Invited session F3-3 (Stolyar): Large-scale and heavy-traffic models (Room 102) . 123- Jhunjhunwala Prakirt: Rate of Convergence of Tail Probabilities to Heavy Traffic 123- Mukherjee Debankur: A Stochastic Approximation Approach to Large-Scale Distributed Rate Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123- Zhong Yuan: Beating the curse of dimensionality in high-dimensional reflecteddiffusion control problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 123- Stolyar Alexander: Data Flow Dissemination in a Network . . . . . . . . . . . . . 124
3.9.4 Contributed session F3-4 (Remiche): Stochastic processes and Markov modeling(Room 103) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124- Yang Yunfang: Load Balancing in Geographically Separated Service Systems . . 124- Dendievel Sarah: Analysis of Singular Perturbation for a Markovian Fluid Model 124- Ali Mohammadi Yeganeh: The Power of a Few Local Samples in Predicting
Epidemics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124- Remiche Marie-Ange: Sojourn time distribution in a fluid queue with server
vacations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
3.9.5 Invited session F3-5 (Agrawal): Learning, Optimization and Applied Probability(Room 104) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125- Sundaresan Rajesh: Learning to detect an anomalous Markov process . . . . . . 125- Juneja Sandeep: Guided top-2 approach to optimally solve the best arm identification problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125- Maguluri Siva Theja: Concentration of Contractive Stochastic Approximation:
Additive and Multiplicative Noise . . . . . . . . . . . . . . . . . . . . . . 126- Agrawal Shubhada: Bandits, Empirical Likelihood Confidence Intervals, andHeavy Tailed Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . 126
3.9.6 Invited session F3-6 (Chen): Learning and queueing theory (Room 105) . . . . . . 126- Hong Guiyu: An online learning method for the staffing problem of Hawkes queues126- Si Nian: Drift control of high-dimensional RBM: A computational method basedon neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127- Zhang Hailun: The Generalized c/µ Rule for Queues with Heterogeneous Server
Pools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127- Cheung Wang Chi: Non-Stationary Bandits with Knapsack Problems with Advice 127
3.9.7 Invited session F3-7 (Shneer & Zocca): Mathematics of energy (Room 106) . . . . 127- Christianen Mark: Comparison of stability regions for a line distribution networkwith stochastic load demands . . . . . . . . . . . . . . . . . . . . . . . . . 127- Moriarty John: The structure of optimal stopping problems under risk . . . . . . 128- Van Der Sar Erica: Optimizing Power Grid Control with Reinforcement Learning 128- Zocca Alessandro: Generating synthetic power grids using Exponential Random
Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
3.9.8 Invited session F3-8 (Varma & Zubeldia): Stochastic Matching Networks and their
Applications (Room 202) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129- Hillas Lisa: Designing Service Menus for Bipartite Qeueing Systems . . . . . . . 129- Wang Qiong: A Stochastic Tracking Model: Asymptotic Analysis and Applications129- Yang Xiaotang: Human in The Loop Automation: Ride-Hailing with Remote(Tele-) Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129- Varma Sushil: Electric Vehicle Fleet and Charging Infrastructure Planning . . . 130
3.9.9 Invited session F3-9 (Zähle): Stability and statistical inference of stochastic optimization problems in quantitative risk management (Room 203) . . . . . . . . . . 130- Krätschmer Volker: Nonasymptotic error rates of the sample average approximation method to solve risk averse stochastic progams . . . . . . . . . . . . 130- Wiesel Johannes: Sensitivity analysis of Wasserstein distributionally robust optimization problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130- Xu Huifu: Robust Spectral Risk Optimization When Information on Risk Spectrum Is Incomplete . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131- Zähle Henryk: A concept of copula robustness and its applications . . . . . . . . 131
3.9.10 Contributed session F3-10 (Minabutdinov): Contagion and Systemic risk (Room204) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131- Hakim Arief: Conditional quantile and expectile-based systemic risk measuresand systemic networks for dirty and clean energy markets . . . . . . . . . 131- Lagos Guido: An asymptotic systemic failure time result for mixed coherent systems132

CONTENTS

13- Amanihamedani Alireza: Spatial Matching under Resource Competition . . . . . 132- Minabutdinov Aleksei: Misfortunes Never Come Singly: Managing the Risk of
Chain Disasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

4 Abstracts of posters
133
4.1 Poster session I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
4.1.1 Bharti Bharti: Spatial appointment scheduling in a random environment . . . . . 133
4.1.2 Brodu Virgile: An individual-based model for food webs: how can simple biologicalconstraints enforce precise allometric coefficients. . . . . . . . . . . . . . . . . . . . 133
4.1.3 Cocomello Juniper: Tractable Approximations of non-Markovian S(E)IR dynamics
on locally tree-like graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
4.1.4 Deep Vikas: Optimal inference of ATE in A/B testing . . . . . . . . . . . . . . . . 134
4.1.5 Deka Prabhanka: PageRank Nibble on the sparse directed stochastic block model . 134
4.1.6 Gupta Hritika: Expected Number of Call Abandonments in a Call Centre . . . . . 134
4.1.7 Hanukov Gabi: Postponed Complementary Services . . . . . . . . . . . . . . . . . 135
4.1.8 Hillas Lisa: Designing Service Menus for Bipartite Qeueing Systems . . . . . . . . 135
4.1.9 Huang Junfei: Capacity Allocation and Scheduling in Two-Stage Service Systems
with Multi-Class Customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
4.1.10 Johlke Anika: Control Charts for Statistical Process Control - A Literature Overview136
4.1.11 Khodadadian Sajad: Federated reinforcement learning: Linear speedup under Markovian sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
4.1.12 Koopmans Camiel: Optimal service control and observation restoration decisionsin Markovian queues with fallible sensors. . . . . . . . . . . . . . . . . . . . . . . . 136
4.1.13 Kraakman Yanna: Configuration models of random directed hypergraphs . . . . . 136
4.1.14 Lee Junghyun: Communication-Efficient Collaborative Heterogeneous Bandits in
Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
4.1.15 Nagy Oliver: Mixing of fast random walks on dynamic random permutations . . . 137
4.1.16 Nieto Benoît: Estimation of threshold diffusions . . . . . . . . . . . . . . . . . . . 137
4.1.17 Qu Yanlin: Contractive Drift Conditions for Rates of Convergence of Markov
Chains in Wasserstein Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.1.18 Reid Mirabel: Convergence of the k-cap Process on Graphs with Weight Reciprocity138
4.1.19 Schmocker Philipp: Universal Approximation Property of Random Neural Networks 138
4.1.20 Vande Velde Sylvie: Branching Processes and Approximate Bayesian Computation
Applied to the Initiation of Chronic Myeloid Leukaemia . . . . . . . . . . . . . . . 138
4.1.21 Van Der Sar Erica: Optimizing Power Grid Control with Reinforcement Learning . 139
4.1.22 Wang Xingyu: Eliminating Sharp Minima from SGD with Truncated Heavy-tailed
Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
4.1.23 Zhang Dongni: Epidemic models with manual and digital contact tracing . . . . . 140
4.2 Poster session II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.2.1 Abdelhakmi Anas: A multi-period Black-Litterman model with desynchronizedexpert views . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
4.2.2 Allmeier Sebastian: Bias of Multiscale Mean Field Models and Averaging Methods 141
4.2.3 Amanihamedani Alireza: Spatial Matching under Resource Competition . . . . . . 141
4.2.4 Bhambay Sanidhay: Asymptotic Optimality of Speed-Aware JSQ for Heterogeneous
Service Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
4.2.5 Blomerus Wessel: Structured learning of the optimal replacement rule beyond
Markov settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
4.2.6 Briend Simon: Finding adam under polynomial connectivity assumption . . . . . . 142
4.2.7 Cheikhi David: On the Statistical Benefits of Temporal Difference Learning . . . . 142
4.2.8 Corujo Rodriguez Josué: The standard augmented multiplicative coalescent revisited142
4.2.9 Ertel Sebastian: Ensemble Kalman-Bucy filters for finite and infinite dimensionalsignals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
4.2.10 Jhunjhunwala Prakirt: Join-the-Shortest Queue with Abandonment: Critically
Loaded and Heavily Overloaded Regimes . . . . . . . . . . . . . . . . . . . . . . . 143
4.2.11 Joshi Purva: Performance Evaluation of Intersection Access Mechanisms for Autonomous Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
4.2.12 Kalvit Anand: Diffusion limits of multi-armed bandit experiments under optimismbased policies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.2.13 Karunarathne Wathsala: Scheduling customers in the presence of random customers into stochastic server systems . . . . . . . . . . . . . . . . . . . . . . . . . . 144
4.2.14 Lee Seunghyun: Fluctuations in random field Ising models . . . . . . . . . . . . . . 144
4.2.15 Loubaton Rodolphe: Modelisation of dynamic gene regulatory networks and prediction of biological experiments modifying genes in cancerous cells. . . . . . . . . 145

CONTENTS

4.3

14

4.2.16 Marek Oheim: Risk Class Management in Life Insurance Markets . . . . . . . . . 145
4.2.17 Miguelez Fernando: Price of Anarchy in an Age-aware multi-source system . . . . 145
4.2.18 Pakyari Reza: Spacings-Based Goodness-of-Fit Testing . . . . . . . . . . . . . . . . 146
4.2.19 Paraggio Paola: A perturbed Bertalanffy-Richards growth: deterministic and stochastic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
4.2.20 Sarkovic Andjela: Phase transition for random walks on graphs with added weighted
random matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
4.2.21 Shadmi Yonatan: Importance sampling in high dimensions . . . . . . . . . . . . . 147
4.2.22 Sun Maotong: Towards Safe Extubation Decision Making for Mechanically Ventilated Patients in ICUs: A Deep Constrained Offline Reinforcement Learning Approach147
4.2.23 Varma Sushil: A Heavy Traffic Theory of Matching Queues . . . . . . . . . . . . . 148
4.2.24 Wei Zhaoxuan: Online Inventory Control with Partial Backorder . . . . . . . . . . 148
4.2.25 Yu Sophie: Constant Regret Primal-Dual Policy for Multi-way Dynamic Matching 148
4.2.26 Zhe Su: Sample path large deviations for Levy processes and random walks with
log-normal increments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
4.2.27 Zhong Yueyang: Learning the Scheduling Policy in Time-Varying Multiclass Many
Server Queues with Abandonment . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
4.2.28 Zur (Schwarz) Elisheva: Priority queues with arrival rate uncertainty . . . . . . . . 149
Poster session III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.3.1 Baars Justin: Delayed Hawkes infinite server queue: excitation delayed by sojourntime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.3.2 Chenchen Xing: Estimating customer valuation in a service system with unobserved
balking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
4.3.3 Dieleman Nanne: A Pseudo-Gradient Approach for Parameter-free Optimizationof Cost Functions over the Stationary Distribution of a Discrete Time Markov Chain150
4.3.4 Janicka Agnieszka: Buffer content analysis of a two-class tandem fluid model . . . 151
4.3.5 Le Corre Thomas: Feature Projection for Optimal Transport . . . . . . . . . . . . 151
4.3.6 Levering Nikki: Externalities in the M/G/1 queue: LCFS-PR versus FCFS . . . . 151
4.3.7 Loeser Eva: Fluid Limit for a Stochastic Model of Enzymatic Processing with
General Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
4.3.8 Luo Yuwei: A Geometric Approach to Improve the Worst Case Performance ofThompson Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
4.3.9 Mcarthur Harry: Preserving Privacy while Publishing Information . . . . . . . . . 152
4.3.10 Mohammadi Narges: Efficient Discovery of Cost-effective Policies in Sequential,
Medical Decision-Making Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
4.3.11 Pijnappel Tom: Optimal Deployment of Drone Base Stations in Wireless Networks 153
4.3.12 Ray Rounak: Percolation on Preferential Attachment Models . . . . . . . . . . . . 153
4.3.13 Shah Abhin: On counterfactual inference with unobserved confounding . . . . . . 153
4.3.14 Van Santvoort Mike: Mathematically mapping the network of cells in the tumor
microenvironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
4.3.15 Van Werde Alexander: Universality-based concentration for sums of dependent
random matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
4.3.16 Wellalage Achini: A Monte Carlo simulation-based simulated annealing algorithm
for predicting the minimum staffing requirement at a blood donor centre . . . . . . 154
4.3.17 Yeung Lane Chun: A non-asymptotic perspective on mean field control via logconcavity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
4.3.18 Zhang Kelly: Inference after Using RL to Learn Across Users for Digital Interventions155
4.3.19 Zhu Feng: Stochastic Multi-armed Bandits: Trade-off among Optimality, Consistency, and Tail Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
4.3.20 Zhou Han: Optimal starting for Renal Replacement Therapy in Intensive Care Unit 156

Committees
Scientific committee
Urtzi Ayesta (CNRS - IRIT)Charles Bordenave (CNRS / Université Aix-Marseille, France)
Madalina Deaconu (Inria / Université de Lorraine, France)
Laurent Decreusefond (Telecom Paristech, France)
David A. Goldberg (Cornell University, USA)
Rouba Ibrahim (UC London, UK)
Matthieu Jonckheere (CNRS - LAAS, France)
Ger Koole (Vrije Universiteit Amsterdam, Nederland)
Antoine Lejay (Inria / Université de Lorraine, France)
Pascal Moyal (Université de Lorraine, France)
Mariana Olvera-Cravioto (UNC Chapel Hill, USA)
Ohad Perry (Northwestern University, USA)
Viet Chi Tran (Université Gustave Eiffel, France)
Amandine Veber (CNRS / Université Paris Cité, France)

Organizing committee
Jocelyn Begeot (Université de Lorraine, France)
Nathalie Benito (Université de Lorraine, France)
Coralie Fritsch (Inria / Université de Lorraine, France)
Sara Mazzonetto (Université de Lorraine, France)
Pascal Moyal (Université de Lorraine, France) - Head organizer
Marie-Sophie Nourdin (Université de Lorraine, France)
Edouard Strickler (CNRS / Université de Lorraine, France)
Local crew:
Virgile Brodu (Inria / Université de Lorraine, France)
Victor Dubach (Université de Lorraine, France)
Jules Flin (Université de Lorraine, France)
Mathilde Gaillard (Inria / Université de Lorraine, France)
Mohamed Amine Hazzami (Université de Lorraine, France)
Saad Jamiri (Université de Lorraine, France)Loïc Jean (Université de Lorraine, France)
Angelo Efoévi Koudou (Université de Lorraine, France)
Rodolphe Loubaton (Inria / Université de Lorraine, France)
Thomas Masanet (Université de Lorraine, France)
Pierre Mercuriali (Inria / Université de Lorraine, France)
Raphael Mignot (Université de Lorraine, France)
Anouk Rago (Inria / Université de Lorraine, France)
Christophe Reype (Inria / Université de Lorraine, France)
Pierrick Siest (Université de Lorraine, France)Saïd Toubra (Inria / Université de Lorraine, France)
Nicolás Zalduendo (Inria / Université de Lorraine, France)

15

Sponsors
The organizers gratefully acknowledge the support of the following organizations:16

Program at a glance
Wednesday, June 28th
8:30
8:50
9:00 - 10:00
10:30 - 12:00
13:30 - 15:00
15:30 - 17:00
17:15 - 18:15
18:30
19:15

Registration opens
Welcome Speech
Plenary talk : Frédéric Chazal
Coffee break
Parallel sessions
Lunch break
Tutorial lecture : Marc Lelarge & Parallel sessions
Coffee break
Parallel sessions & Posters session
Mini break
Plenary talk : Beatrice Meini
Mini break
APS Business Meeting
Cocktail at Centre Prouvé

Thursday, June 29th
8:30
9:00 - 10:00
10:30 - 12:00
13:30 - 15:00
15:30 - 17:00
17:15 - 18:15
19:00

Registration opens
Plenary talk : Sylvie Méléard
Coffee break
Parallel sessions
Lunch break
Parallel sessions
Coffee break
Parallel sessions & Posters session
Mini break
Plenary talk : Sean Meyn
Gala dinner

Friday, June 30th
8:30
9:00 - 10:00
10:30 - 12:00
13:30 - 15:00
15:30 - 17:00
17:15

Registration opens
Plenary talk : Amy Ward
Coffee break
Parallel sessions
Lunch break
Parallel sessions
Coffee break
Parallel sessions & Posters session
Mini breakConclusion

17

Mini
Break

17h00

19h15

18h30

18h15

APS
Business
Meeting

W3-2
Burnetas

W2-2
Zhan

W1-2
Haviv

Room 101

W3-3
Maguluri

W2-3
Dong

W1-3
IbrahimRoom 102

Contributed session

Marcel Neuts
LectureW3-1
Dorsman/Righter/Gardner I

Tutorial
LectureW1-1
Baccelli/Foss

Plenary talk

Mini
Break

Postersession

15h30

17h15

Coffee
Break

Lunch
Break

15h00

13h30

12h00

10h30

CoffeeBreak

Plenary talk
F. Chazal

9h00

10h00

Welcoming
speechAuditorium

8h50

Foyer 850

W3-6
Xu

W2-6
Namkoong

W1-6
HarshaRoom 105

Tutorial lecture

W3-8
Banerjee

W2-8
Weiss

W1-8
Adan

Room 202

Poster session

W3-7
GoulardW2-7
Litvak

W1-7
Ben-Hamou

Room 106

Cocktail reception
Panoramic salon of Centre Prouvé

W3-5
OlveraCravioto I

W2-5
Fraiman

W1-5
Robert I

Room 104

Invited session

W3-4
Cohen

W2-4
Fricker

W1-4Champagnat

Room 103• WEDNESDAY, JUNE 28 •W3-9
Daw/Yom-Tov

W2-9
Loisel

W1-9
Coculescu

Room 203

W3-10
Brown

W2-10
Mijatovic

W1-10
Lim

Room 204

W1-11
Garcia Pareja

W2-11
ValloisW1-11
Yao

Room 205

CONTENTS
18

Mini
Break

17h00

19h00

IMS Medallion
LectureS. Méléard

Auditorium

Room 101
Room 102

T3-2
Koole

T2-2
Kanavetas

T3-3
Ayhan

T2-3
Buke

Contributed session

Applied Probability
Trust Lecture
S. MeynT3-1
Blanchet

T2-1
Ramanan

T1-1
T1-3
T1-2
Avrachenkov /Hasenbein Castellanos
AyestaPlenary talk

Postersession

15h30

17h15

Coffee
Break

Lunch
Break

Coffee
Break

15h00

13h30

12h00

10h30

10h00

9h00

Foyer 850

T3-6
Wang

T2-6
Sanders

T1-6
Shi

Room 105

Tutorial lecture

T1-8
Dorsman/Righter/Gardner II

Room 202

T3-8
Comte

Poster session

T3-7
Backhausz

T2-8
T2-7
Dorsman/Righter
Avrachenkov /Gardner III

T1-7
OlveraCravioto II

Room 106

Gala DinnerGrands Salons de l’Hôtel de Ville

T3-5
Penington

T2-5
Henry

T1-5
Coron

Room 104

Invited session

T3-4
Tran

T2-4
Nazarathy

T1-4
Baccelli/
Foss II

Room 103• THURSDAY, JUNE 29 •Room 203

T3-9
Decreusefond II

T2-9
Decreusefond I

T1-9
TorrisiRoom 204

Room 205

CONTENTS
19

Mini
Break

17h00

Auditorium

F3-1
LelargeF3-2
Huang/Xie

F2-2
RavnerF1-2
Stolletz

Room 101

F3-3
Stolyar

T2-3
Buke

F1-3
Henderson

Room 102

Contributed session

Tutorial lecture
S. Penington

F1-1
Robert II

Plenary talk
A. WardPlenary talk

Postersession

15h30

17h15

Coffee
Break

Lunch
Break

Coffee
Break

15h00

13h30

12h00

10h30

10h00

9h00

Foyer 850

Room 105

F3-6
Chen

F2-6
Hautphenne

Tutorial lecture

F3-5
AgrawalF2-5
Bhulai

F1-6
F1-5
De Saporta Jonckheere

Room 104

Invited session

F3-4
RemicheF1-4
Wang

Room 103

F3-8
Varma /
Zubeldia

F2-8
Busic

F1-8
PerchetRoom 202

Poster session

F3-7
Shneer/Zocca

F2-7
LeskelaF1-7
HachemRoom 106• FRIDAY, JUNE 30 •

F3-9
Zähle

F2-9
Carrassus

F1-9
Kazi-Tani

Room 203

F3-10
Hakim

F2-10
Rhee

F1-10
MurthyRoom 204

Room 205

CONTENTS
20

List of invited session organizers• Adan, Ivo (Eindhoven University of Technology, Netherlands) - Random walks and product forms• Agrawal, Shubhada (Georgia Institute of Technology, United States)- Learning, Optimization andApplied Probability• Avrachenkov, Kostia (Inria Sophia Antipolis, France)- Network inference I• Avrachenkov, Kostia (Inria Sophia Antipolis, France)/ Ayesta, Urtzi (CNRS IRIT, France)- Reinforcement learning• Ayhan, Hayriye (Georgia Institute of Technology, United States) - Control of Queueing Systems• Baccelli, François (Inria, France) / Foss, Sergey (Heriot-Watt University, United Kingdom) Infinite-dimensional queueing/communication systems• Baccelli, François (Inria, France) / Foss, Sergey (Heriot-Watt University, United Kingdom) - Meanfield approach for analysis of complex stochastic systems I• Backhausz, Agnes (Eötvös Loránd University, Hungary) - Random graphs and matrices• Banerjee, Sid (Cornell University, United States) - Online decision making• Ben Hamou, Anna (Sorbonne University, LPSM, France) - Random walks on random graphs• Bhulai, Sandjai (Vrije Universiteit Amsterdam, Netherlands)- Modern techniques in Markov decision problems• Brown, David (Duke University, United States) - Advances in Experimentation and Dynamic Models• Buke, Burak (The University of Edinburgh, United Kingdom)- Many-Server Queues• Burnetas, Apostolos (National and Kapodistrian University of Athens, Greece) - Strategic queueing
III• Busic, Ana (ENS/Inria, France) - Stochastic matching and applications• Carassus, Laurence (Ecole Supérieure d’Ingénieurs Léonard De Vinci, France) - Robust finance• Castellanos, Antonio (University of Chicago, United States) - Queueing Systems with Predictions• Champagnat, Nicolas / Villemonais, Denis (Inria Nancy/Université de Lorraine) - Quasi-stationary
distributions in numerical stochastic methods and statistics• Chen, Xinyun (CUHK Shenzhen, China) - Learning and queueing theory• Coculescu, Delia (University of Zurich, Switzerland) - Risk measures in finance and insurance• Cohen, Asaf (University of Michigan, United States) - Mean-Field Limits and their Applications• Comte, Céline (CNRS, France) - Online stochastic matching: a diversity of viewpoints• Coron, Camille (Université Paris Sud, France) - Stochastic models for ecology and evolution• Daw, Andrew (University of Southern California, United States) / Yom-Tov, Galit (Technion Israel Institute of Technology, Israel) - Hawkes Processes: New Models and Theory• De Saporta, Benoîte (Université de Montpellier, France) - Learning while controling: crossroads
between stochastic control and statistical learning• Decreusefond, Laurent (Télécom Paris, France) - Stein method I
21

CONTENTS

22• Decreusefond, Laurent (Télécom Paris, France) - Stein method II• Dong, Jing (Columbia University, United States) - Efficient scheduling and resource allocation• Dorsman, Jan-Pieter (University of Amsterdam, Netherlands)/ Righter, Rhonda (IEOR UC Berkeley, United States) / Gardner, Kristen (Amherst College, United States) - Load balancing I• Dorsman, Jan-Pieter (University of Amsterdam, Netherlands)/ Righter, Rhonda (IEOR UC Berkeley, United States) / Gardner, Kristen (Amherst College, United States) - Load balancing II• Dorsman, Jan-Pieter (University of Amsterdam, Netherlands)/ Righter, Rhonda (IEOR UC Berkeley, United States) / Gardner, Kristen (Amherst College, United States) - Load balancing III• Fraiman, Nicolas (University of North Carolina at Chapel Hill, United States) - Algorithms on
random graphs• Fricker, Christine (Inria Paris, France) - Mean-field approach for analysis of complex stochastic
systems II• Goulart, Henrique (IRIT, France) - Random matrices and random tensors in data analysis andmachine learning• Hachem, Walid (CNRS, Université Gustave Eiffel, France) - Large random matrices and statisticallearning• Hasenbein, John (The University of Texas at Austin, United States) - Strategic queueing IV• Haviv, Moshe (Chinese University of Hong Kong, Shenzhen, China, and Hebrew University ofJerusalem, Israel) - Strategic queueing I• Henderson, Shane (Cornell University, United States) - Healthcare• Henry, Benoît (Institut Mines Télécom, France) - Stochastic geometry• Hu, Yue (University of Chicago, United States) - Queueing theory and its applications• Huang, Junfei (The Chinese University of Hong Kong, China)/ Xie, Jingui (Technical Universityof Munich, Germany)- Stochastic models and their application• Ibrahim, Rouba (UCL, United Kingdom) - Scheduling in service systems• Jonckheere, Matthieu (CNRS, France) - Probabilistic tools for learning• Kanavetas, Odysseos (Leiden University, Netherlands) - Strategic queueing V• Kazi-Tani, Nabil (Université de Lorraine, France)- Recent advances in actuarial science• Koole, Ger (Vrije Universiteit, Amsterdam, Netherlands) - Call centers• Lelarge, Marc (Inria, France)- Graph matching and planted models in random graphs• Leskela, Lasse (Aalto University, Finland) - Network inference II• Litvak, Nelly (University of Twente, Netherlands) - Spatial network models• Loisel, Stéphane (ISFA, Univ. Lyon 1, France) - Actuarial mathematics and quantitative risk
management• Maguluri, Siva Theja (Georgia Institute of Technology, United States)- Stochastic processing networks• Mijatovic, Aleks (University of Warwick, United Kingdom)- Simulation, Estimation and Monte
Carlo Algorithms for Levy Processes• Murthy, Karthyek (Singapore University of Technology and Design, Singapore)- Advances in stochastic modelling for decision-making under uncertainty• Namkoong, Hongseok (Columbia Business School, United States) - Frontiers in sequential learning• Nazarathy, Yoni (The University of Queensland, Australia) - Applied Probability for COVID-19• Olvera-Cravioto, Mariana (University of North Carolina, Chapel Hill, United States) - Graph dynamics

CONTENTS

23• Olvera-Cravioto, Mariana (University of North Carolina, Chapel Hill, United States) - Processeson graphs• Penington, Sarah (University of Bath, United Kingdom) - Branching processes and their applications• Perchet, Vianney (ENS Paris-Saclay & criteo AI Lab, France) - Online and dynamic matchings• Ramanan, Kavita (Brown University, United States) - Measure-valued process limits of stochastic
networks• Ravner, Liron (University of Haifa, Israel) - Statistical inference for queueing systems• Rhee, Chang-Han (Northwestern University, Unites States) - Rare Events: Large Deviations, Simulation, and AI• Robert, Philippe (Inria, France) - Stochastic Chemical Reaction Networks• Robert, Philippe (Inria, France) - Stochastic Models of Biology• Sanders, Jaron (Eindhoven University of Technology, Netherlands) - Stochastic learning in structured systems• Shi, Pengyi (Purdue University, United States) - Learning algorithms for managing service systems• Shneer, Seva (Heriot-Watt University, United Kingdom) / Zocca, Alessandro (Vrije Universiteit
Amsterdam, Netherlands) - Mathematics of energy• Stolletz, Raik (University of Mannheim, Germany) - Stochastic modeling of manufacturing andservice operations• Stolyar, Alexander (University of Illinois at Urbana-Champaign, United States) - Large-scale andheavy-traffic models• Torrisi, Giovanni (CNR-IAC, Rome, Italy) - Topics on point processes models : theory and applications• Tran, Viet Chi (Université Gustave Eiffel, France) - Contact-tracing• Varma, Sushil (Georgia Institute of Technology, United States)/ Zubeldia, Martin (University of
Minnesota, United States)- Stochastic Matching Networks and their Applications• Wang, Weina (Carnegie Mellon University, United States) - Queues, bandits, and reinforcement
learning• Weiss, Gideon (The University of Haifa, Israel) - Applications of Dynamic Matching• Xu, Kuang (Stanford University, United States) - Experimentation and Learning with Stochastic
Models• Zähle, Henryk (Saarland University, Germany) - Stability and statistical inference of stochastic
optimization problems in quantitative risk management• Zhan, Dongyuan (UCL, United Kingdom) - Strategic queueing II

List of speakers
Abdelhakmi Anas
Adan Ivo
Afèche Philipp
Agarwal Anish
Agrawal Shubhada
Ali Mohammadi Yeganeh
Allmeier Sebastian
Amanihamedani Alireza
Anton Elene
Aouad Ali
Araya Valdivia Ernesto
Arlotto Alessandro
Asanjarani Azam
Ata Baris
Atar Rami
Avramidis Thanos
Ayhan Hayriye
Baars Justin
Baccelli Francois
Banerjee Siddhartha
Barigou Karim
Baron Opher
Bastani Hamsa
Bayati Mohsen
Bekker Rene
Ben-Hamou Anna
Bhambay Sanidhay
Bhamidi Shankar
Bharti Bharti
Bhulai Sandjai
Bignozzi Valeria
Bindiganavile Ramadas Vinay
Kumar
Blaszczyszyn Bartlomiej
Blomerus Wessel
Bodas Shreehari
Bodas Tejas
Borkar Vivek
Borst Sem
Boucherie Richard
Bountali Olga
Braun Alexander
Briend Simon
Britton Tom
Brodu Virgile
Brown David
Buke Burak
Burnetas Apostolos
Caldentey Rene
Campillo Fabien
Cappelletti Daniele

Carassus Laurence
Flint Ian
Cardinaels Ellen
Foss Sergey
Castellanos Antonio
Fourneau Jean-Michel
Castiel Eyal
Fritsch Coralie
Chazal FrédéricGanassali Luca
Che Ethan
Ganguly Ankan
Chehrazi Naveed
Gao Xuefeng
Cheikhi David
Garcia Pareja Celia
Chen Xinyun
Gardner Kristen
Chen Yilun
Garlati Elena
Chenchen Xing
Gast Nicolas
Cheung Wang Chi
Gaujal Bruno
Chick Stephen
Ger Koole
Christianen Mark
Gerencsér Balázs
Clarkson Jake
Ghosh Soumyadip
Cohen Asaf
Goffard Pierre-Olivier
Comte Céline
Goldsztajn Diego
Contat Alice
Gonzalez Casanova Adrian
Corujo Rodriguez Josué
Gonzalez Cazares Jorge
Costa Manon
Grigorova Miryana
Coupier David
Gupta Hritika
Coutin LaureGösgens Martijn
Curien Nicolas
Hakim Arief
Dai Jim
Halconruy Hélène
Das Bikramjit
Hanukov Gabi
Davydov Michel
Hartung Lisa
Daw Andrew
Hassin Refael
De Turck Koen
Haugh Martin
Decreusefond Laurent
Hautphenne Sophie
Deep Vikas
Haviv Moshe
Deka Prabhanka
Henderson Shane
Dendievel Sarah
Heydenreich Markus
Denis Laurent
Hillairet Caroline
Deo Anand
Hillas Lisa
Diallo Aoudi Mohamed Habib Holmgren Cecilia
Aliou
Hong Guiyu
Dieleman Nanne
Honnappa Harsha
Dimitrakopoulos Yiannis
Hu YueDimitriou Ioannis
Huang Junfei
Dong Jing
Huang Zhiyuan
Dongyuan Zhan
Hyytiä Esa
Doroudi Sherwin
Ibrahim Rouba
Dreveton Maximilien
Jacko Peter
Dwivedi Raaz
Jahnel Benedikt
Economou Antonis
Janicka Agnieszka
Ecoto Geoffrey
Jhunjhunwala Prakirt
El Masmari Soukaina
Jiang Jiashuo
Ertel SebastianJohlke Anika
Fan ZhouJonckheere Matthieu
Feng Yifan
Joshi Purva
Ferragut Andres
Juneja Sandeep
24

25

CONTENTS
Kalvit Anand
Kanavetas Odysseas
Karabag Oktay
Karunarathne Wathsala
Kazi-Tani Nabil
Keppo Jussi
Kerimov Süleyman
Kerner Yoav
Khayyati Siamak
Khezeli Ali
Khodadadian Sajad
Kirchner Matthias
Kobeissi Ziad
Kohlenberg Angela
Koopmans Camiel
Kraakman Yanna
Krätschmer Volker
Kumar Rachitesh
Lagos Guido
Laurence Lucie
Lauriere Mathieu
Le Corre Thomas
Le Quellennec Orlane
Lebeau Hugo
Lee Junghyun
Legros Benjamin
Lelarge Marc
Leskelä Lasse
Levering Nikki
Lewis MarkLi Hannah
Li Shuangning
Li Siqiao
Liberopoulos George
Lim Andrew
Lim Eunji
Lin Feng
Litvak Nelly
Loeser Eva
Logothetis Dimitrios
Loisel Stéphane
Lomys Niccolò
Loubaton Philippe
Loubaton Rodolphe
Lu Yingdong
Luo Yuwei
Lykouris Thodoris
Maggiori Andreas
Maguluri Siva Theja
Mai Xiaoyi
Maillard Antoine
Maillard Odalric-Ambrym
Maillard Pascal
Mandjes Michel
Manou Athanasia
Mao Cheng
Marek Oheim
Marguet Aline
Mariucci Ester
Mastropietro Daniel
Mathien Joffrey
Matias Catherine
Mcarthur Harry

Meini Beatrice
Méléard Sylvie
Mertz Laurent
Meyn Sean
Miao Sentao
Miguelez Fernando
Mijatovic Aleksandar
Minabutdinov Aleksei
Mohamed Hanène
Mohammadi Narges
Moriarty John
Mouchtaki Omar
Moyal Pascal
Mukherjee Debankur
Mukhopadhyay Arpan
Murthy Karthyek
Müller Noela
Nagy Oliver
Namkoong Hongseok
Neufeld Ariel
Nieto Benoît
Nourdin Ivan
Olvera-Cravioto Mariana
Oz Binyamin
Pain Michel
Pakyari Reza
Pal Chandan
Paraggio Paola
Peccati Giovanni
Penington Sarah
Perarnau Guillem
Pergamenchtchikov Serguei
Perry Ohad
Pfaffelhuber Peter
Pham Huyên
Pijnappel Tom
Popovic Lea
Privault Nicolas
Qian Pengyu
Qu Yanlin
Ramanan Kavita
Ravner Liron
Ray Rounak
Rebuffi Louis-Sébastien
Reid Mirabel
Reiffenhäuser Rebecca
Remiche Marie-Ange
Reveillac Anthony
Rhee Chang-Han
Righter Rhonda
Robledo Francisco
Rochlin Igor
Rosazza Gianin Emanuela
Saha Subhamay
Sanders Jaron
Sankararaman Abishek
Sarhangian Vahid
Sarkovic Andjela
Schmocker Philipp
Schwarz Justus Arne
Scully Ziv
Seddik Mohamed El Amine
Senen-Cerda Albert

Sentenac Flore
Shadmi Yonatan
Shah Abhin
Shi Pengyi
Shneer Seva
Si NianSingh Simrita
Singh Vikas Vikram
Skerman Fiona
Snitkovsky Ran
Spieksma Floske
Srikant R
Stegehuis Clara
Stephan Ludovic
Stolyar Alexander
Strickler Edouard
Sun Maotong
Sundaresan Rajesh
Swan Yvik
Tercieux Olivier
Tourniaire Julie
Toyoizumi Hiroshi
Trottner Lukas
Vallois Pierre
Van Der Hoorn Pim
Van Der Mei Rob
Van Der Sar Erica
Van Eekelen Wouter
Van Santvoort Mike
Van Werde Alexander
Vande Velde Sylvie
Varma Sushil
Vignoud Gaetan
Villemonais Denis
Vo Thi Phuong Thuy
Vuong Christophe
Véber Amandine
Walraevens Joris
Walton Neil
Wang Jinting
Wang Qiong
Wang Tiandong
Wang Weina
Wang Xingyu
Wang Zhe
Wang Zhengli
Wang Zicheng
Ward Amy
Weber Stefan
Weber Thomas
Weedage Lotte
Wei Zhaoxuan
Weiss Gideon
Wellalage Achini
Wichelhaus Cornelia
Wiesel Johannes
Xie Yao
Xu Huifu
Xu Jiaming
Xu Kuang
Yang Xiaotang
Yang Yunfang
Yasodharan Sarath

26

CONTENTS
Yeung Lane Chun
Yom-Tov Galit
You Wei
Yu Sophie
Zaherddine Jana
Zalduendo Vidal Nicolas
Zhang Anderson Ye
Zhang Dongni

Zhang Hailun
Zhang Kelly
Zhe SuZhong Yuan
Zhong Yueyang
Zhou Han
Zhu Feng
Zhu Yizhe

Zocca Alessandro
Zoumpoulis Spyros
Zubeldia Martin
Zur (Schwarz) Elisheva
Zähle HenrykÖzümerzifon Ömer

Chapter 1

Plenary talks
All plenaries take place in Auditorium.

1.1

Frédéric Chazal (Inria Saclay, France)
Wednesday, June 28, 9:00 - 10:00

Clustering of discrete measures via mean measure quantization: application to Topological
Data Analysis
In Topological Data Analysis, robust topological information from datais commonly represented by a discrete measure in the Euclidean plane, theso-called persistence diagram, that needs to be converted into a vector to
be used in classical machine learning pipelines. In this talk, after a brief
introduction to persistent homology, we will introduce a fast, learnt, unsupervised vectorization method for measures in Euclidean spaces and use it
for clustering of distributions of discrete measures. The algorithm is simple
and efficiently discriminates important space regions where meaningful differences to the “mean” measurearise. Applied to persistence diagrams, we will show that it is proven to be able to separate clusters of
persistence diagrams. We will illustrate the strength and robustness of our approach on a few synthetic
and real data sets.

1.2

Beatrice Meini (Univ. Pisa, Italy) - Marcel Neuts Lecture
Wednesday, June 28, 17:15 - 18:15

Matrix-analytic Methods from an algebraic and computational point of view
Matrix-Analytic Methods (MAM) were introduced by Marcel Neuts
in the ’80s and have been applied to a variety of stochastic models, primarily queues but also specific models for dams and inventories.
As Neuts wrote in the review paper Matrix-analytic methods in queueing
theory, European Journal of Operation Research, 1984, “[MAM] originatedin the search for algorithmic methods and has led to results that are wellsuited for computer implementation.”Indeed, MAM allow to compute efficiently the stationary probability distribution of an infinite dimensional Markov chain which has a Toeplitz structure. More specifically, the transition matrix has an
infinite number of block rows and columns (representing transitions between levels) and finite dimensional
blocks (representing transitions between phases). Moreover, the block entries of the transition matrix areconstant along each block diagonal, except for the first block rows or columns. According to MAM, thecomputation of the stationary probability distribution is ultimately reduced to the solution of a suitablenonlinear matrix equation.
Starting from the ’90s, many numerical methods have been developed for the numerical solutionof such nonlinear matrix equations, and the research is still active.
In this talk, we review MAM from an algebraic point of view, by interpreting the methodology
27

CHAPTER 1. PLENARY TALKS

28

introduced by Neuts in terms of infinite dimensional structured linear systems. Also the resulting nonlinear matrix equation can be interpreted by means of a suitable infinite dimensional linear system. This
interpretation allows introducing efficient numerical methods, based on Cyclic Reduction, for solving
the nonlinear matrix equation. We recall classical fixed point iterations, Newton’s method, the cyclic
reduction and logarithmic reduction algorithms and we present the more recent advances, based on the
Quasi-Toeplitz matrix arithmetic, which allow to treat the case of infinite number of phases, like in thequeueing models represented by random walks in the quarter plane.

1.3

Sylvie Méléard (École Polytechnique, France) IMS Medallion Lecture
Thursday, June 29, 9:00 - 10:00

Multiscale eco-evolutionary models: from individuals to populations
Motivated by recent biological experiments, we emphasize the effects of small
and random populations on long time population dynamics. We will quantify such
effects on macroscopic approximations. The individual behaviors are describedby the mean of a stochastic measure-valued process. We study different longtime asymptotic behaviors depending on the assumptions on mutation size andfrequency and on horizontal transmission rate. In some cases, simulations indicatethat these models should exhibit surprising asymptotic behaviors such as cyclic
behaviors. We explore these behaviors on a simple model where population andtime sizes are on a log-scale. Explicit criteria are given to characterize the possible
asymptotic behaviors. The impact of the time and size scales on macroscopic
approximations is also investigated, leading to Hamilton-Jacobi equations.

1.4

Sean Meyn (Univ. Florida, USA) - APT Plenary Lecture
Thursday, June 29, 17:15 - 18:15

Who is Q? A beginner’s guide to reinforcement learning
One theoretical foundation of reinforcement learning is optimal control,
usually the Markovian variety known as Markov decision processes (MDPs).
The MDP model consists of a state process, an action (or input) process,and a one-step cost function that is a function of state and action. The goal
is to obtain a policy (function from states to actions) that is optimal in somepredefined sense. Chris Watkins introduced the Q-function in the 1980s as
part of a methodology for reinforcement learning. Given its importance forover three decades, it is not surprising that the question of the true meaningof Q was a hot topic for discussion during the Simons Institute’s Fall 2020
program on Theory of Reinforcement Learning. In this lecture we discover
the truth about Q’s origins, and what has happened since.
WeâĂŹve all heard about the magic of Q-consider alpha-zero and chatGPT. As we review the foundationsof the reinforcement universe, you may share the speaker’s amazement that Q-learning is ever successful!
This invites many research questions: why does Q-learning result in successful solutions for decisionand control? How can we create new approaches to reinforcement learning that are efficient in terms of
training, and also provide some estimate of policy performance?
The lecture draws on Chapters 5 and 9 of the new monograph, Control Systems and Reinforcement
Learning, as well as recent papers on Convex Q-Learning and Logistic Q-Learning.

1.5

Amy Ward (University of Chicago, USA)
Friday, June 30, 9:00 - 10:00

Queues with (Non-Exponential) Reneging
The study of scheduling problems has a long history in the academic literature. However, many classic models used to study scheduling problems do
not incorporate customer impatience. Furthermore, many of the ones that
do assume the time a customer is willing to wait for service is exponentiallydistributed. The issue is that that assumption can lead to poor schedulingCHAPTER 1. PLENARY TALKS

29

decisions.
In this talk, we discuss the interplay between customer impatience andscheduling decisions when managing heterogeneous customer classes in (1) a many server queue, and(2) a matching model. To do this, we develop measure-valued fluid models that approximate the evolution of the underlying discrete-event model. A key insight from our analysis is that the schedulingdecisions depend critically on the patience time distribution. We end by showing how our fluid model
can be used to simplify scheduling problems in which distributional knowledge is not known in advance,and must be learned.
This talk will cover results in the following papers (all available here):
1. Puha and Ward (2022, MOR), Fluid Limits for Multiclass Many Server Queues with General
Reneging Distributions and Head-of-Line Scheduling
2. Zhong, Birge, and Ward (2023 Working Paper), Learning the Scheduling Policy in Time-Varying
Multiclass Many Server Queues with Abandonment
3. Aveklouris, Puha, and Ward (2023 Working Paper), A Fluid Approximation for a Matching Modelwith General Reneging Distributions
4. Aveklouris, Devalve, and Ward (2023 Working Paper), Matching Impatient and Heterogeneous
Demand and SupplyChapter 2

Tutorial lectures
2.1

Marc Lelarge (Inria Paris, France)
Wednesday, June 28, 13:30 - 15:00

Phase transition for statistical estimation: algorithms and fundamental limits
In this tutorial, we study various statistical problems such as community
detection on graphs, Principal Component Analysis (PCA), sparse PCA,
and Gaussian mixture clustering in a Bayesian framework. Using a statistical physics point of view, we show that there exists a critical noise level
above which it is impossible to estimate better than random guessing. Below this threshold, we compare the performance of existing polynomial-timealgorithms to the optimal one and observe a gap in many situations: even
if non-trivial estimation is theoretically possible, computationally efficientmethods do not manage to achieve optimality. This tutorial will present how
we adapted the tools and techniques from the mathematical study of spinglasses to study high-dimensional statistics.

2.2

Canceled : Sarah Penington (University of Bath, UK)
Friday, June 30, 13:30 - 15:00

Branching random walks with selection
The N -particle branching random walk is a model for a population inwhich each individual has a fitness value, given by a random displacementfrom the fitness of its parent, and only the individuals with highest fitness
survive to produce new offspring. I will discuss results and open conjectures
about the long-term behaviour in this model when N , the total number ofindividuals, is large.

30

Chapter 3

Abstracts of invited talks andcontributed talks
3.1

Wednesday 28th, 10:30 - 12:00

3.1.1

Invited session W1-1 (Baccelli & Foss): Mean-field approach for analysis of complex stochastic systems I (Auditorium)

Wednesday 28th, 10:30 - 12:00 - chaired by François Baccelli & Sergey Foss
Davydov Michel: Propagation of chaos for replica mean-field models of intensity-basedneural networks
Davydov Michel1
1 - Inria de Paris (France)

Abstract: Neural computations arising from myriads of interactions between spiking neurons can bemodeled as network dynamics with punctuate interactions. However, most relevant dynamics do notallow for computational tractability. To circumvent this difficulty, the Poisson Hypothesis regime replaces interaction times between nodes by Poisson processes. This approximation is often conjectured
or numerically validated but not proven. We show that the Poisson Hypothesis holds at the limit of an
infinite number of replicas in the replica-mean-field model, which consists of randomly interacting copiesof the network of interest.
Gast Nicolas: The bias of mean field approximation
Gast Nicolas1,2
1 - Inria Grenoble-Rhône Alpes (France)
2 - Laboratoire d’Informatique de Grenoble (France)

Abstract: Mean field approximation provides a good tool to study a stochastic process. It is constructedby assuming that the particles that compose a system are independent. In this talk, I will talk about
tools that are used to compute the approximation error made when using such an approximation. I will
introduce the notion of generators and how to compare them. This will lead us to talk about Poisson
equation and Stein’s method.
Ramanan Kavita: Limits of empirical measures of interacting jump processes on sparsegraphsRamanan Kavita1 , Ganguly Ankan, Cocomello Juniper
1 - Brown University (United States)

Abstract: We provide characterizations of empirical measure and marginal dynamics of interacting jumpprocesses on large sparse random graphs as solutions to a path-dependent nonlinear evolution equation.
We also show how this leads to principled approximations that shed insight into the behavior of several
concrete models on large random graphs. This is joint work with J. Cocomello and A. Ganguly.

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Baccelli Francois & Foss Sergey: Migration-Contagion Processes
Baccelli Francois1 , Foss Sergey2 , Shneer Seva2
1 - Inria (France)
2 - Heriot-Watt University (United Kingdom)

Abstract: Consider the following migration process based on a closed network of N queues with K =K(N ) customers. Each station is an infinite-server queue with rate-µ exponential service times. Upon
departure, a customer is routed independently and uniformly at random to another station. In addition
to migration, these customers are subject to an SIS (Susceptible, Infected, Susceptible) dynamics. Thatis, customers are in one of two states: I for infected or S for susceptible. Customers can swap theirstate either from I to S or from S to I only in stations. More precisely, at any station, each susceptible
customer becomes infected with the instantaneous rate αY if there are Y infected customers in the station,
whereas each infected customer recovers and becomes susceptible with rate β. We let N and K tend toinfinity, with assuming that K/N → η, where η is a positive constant representing the customer density.
The main problem of interest is about the set of parameters of such a system for which there exists a
stationary regime where the epidemic survives in the limiting system. The latter limit will be referred toas the thermodynamic limit. We establish several structural properties (monotonicity and convexity) of
the system, which allow us to give the structure of the phase transition diagram of this thermodynamic
limit w.r.t. η. The analysis of this SIS model reduces to that of a wave-type PDE for which we foundno explicit solution. This plain SIS model is one among several companion stochastic processes thatexhibit both migration and contagion. Two of them are discussed in the talk as they provide variants
to the plain SIS model as well as some bounds and approximations. These two variants are the DOCS
(Departure On Change of State) and the AIR (Averaged Infection Rate), which both admit closed-form
solutions. The AIR system is a classical mean-field model where the infection mechanism based on theactual population of infected customers is replaced by a mechanism based on some empirical averageof the number of infected customers in all stations. The latter admits a product-form solution. DOCS
features accelerated migration in that each change of SIS state implies an immediate departure. Thismodel leads to another wave-type PDE that admits a closed-form solution. In this text, the main focus
is on the closed systems and their limits. The open systems consisting of a single station with Poisson
input are instrumental in the analysis of the thermodynamic limits and are also of independent interest.

3.1.2

Invited session W1-2 (Haviv): Strategic queueing I (Room 101)

Wednesday 28th, 10:30 - 12:00 - chaired by Moshe Haviv
Haviv Moshe: Intermediate priorities and the balancing of the C-mu rule
Haviv Moshe1,2
1 - Chinese University of Hong Kong, Shenzhen (China)
2 - Hebrew University of Jerusalem (Israel)

Abstract: The Cµ-rule is well-known to be socially optimal in the sense that it minimizes the overall mean
waiting costs due to queueing. Yet, this rule is blind to fairness. In particular, it is possible that those witha low cost of wait per unit of time end up suffering more than those with a corresponding high parameter.
We suggest here a fairer scheme which minimizes the overall cost under the constraint that this anomaly
does not exist. It is based on partitioning customers’ classes into leagues, such that absolute priority is
granted among the leagues a-la the Cµ-rule, while within leagues relative or accumulated priorities areused. Towards that end we firstly revisited some results on such priority schemes and derived some newones.
Hassin Refael: Optimal and Self Selection of Service Type in a Queueing System where
Long Service Postpones the Need for the Next Service
Hassin Refael1 , Wang Jiesen2
1 - Tel Aviv University [Tel Aviv] (P.O. Box 39040, Tel Aviv 6997801 Israel)
2 - Tel Aviv University (Tel Aviv Israel)

Abstract: We study a make-to-order system with a finite set of customers. Production is stochastic
with a nonlinear dependence between the ordered quantity and production rate. Customers may have toqueue until their turn arrives and therefore their order decisions interact. Specifically, while being served,
customers are aware of the queue length and choose one of two order quantities (or, service types). The
time to the next replenishment (their activity time) is stochastic and depends on the order quantities.

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A customer is inactive during service and wheile waiting in the queue. We refer to the service type witha greater ratio of expected activity to service time as more efficient. Customers wish to maximize their
proportion of activity time, while the system is interested in maximizing the steady-state average numberof active customers, which is referred to as system efficiency. We show that choosing the more efficient
service is not always socially optimal. We demonstrate that if we constrain the strategy to be a thresholdstrategy depending on the number of inactive customers, then the resulting maximal system efficiency
is close to optimal. We also study individuals’ optimal selection of the service type, and observe that
individuals and the manager have opposite incentives: when the queue is long, individuals tend to choose
the long service, while the manager prefers the short service in this case. This makes the system hard toregulate. However, we show that simply removing the less efficient service yields an efficient behavior.
Clarkson Jake: On the Price of Information in Queueing
Clarkson Jake1 , Avrachenkov Konstantin1 , Altman Eitan1
1 - Centre Inria d’Université Côte d’Azur (France)

Abstract: Consider an M/M/1 queue where joining attains a known reward, but a known cost is paid
for each time unit spent waiting for service. In the 1960s, Naor showed that an arrival optimally joins
the queue if and only if its length is less than a known threshold. Yet often the queue length cannot
be observed without some effort, for example, access of a website or travel to the queue location itself.
Therefore, we assume that the queue length can only be learned by paying an additional known cost. Any
arrival hence has three options: join blindly, balk blindly, or pay for queue-length information to makethe optimal joining decision using Naor’s threshold. The value of knowing the queue length depends on
the joining strategies of other arrivals. In a recent paper, Hassin and Roet-Green prove that a unique
Nash equilibrium always exists and classify regions where the equilibrium probabilities are non-zero. We
simplify some of these findings and obtain closed-form expressions for the equilibrium probabilities incertain cases. Further, Hassin and Roet-Green show that social welfare is maximised by minimising thecost of learning the queue length. We compare the effect on social welfare of lowering this queue-length
cost with increasing the queue-joining reward. We show that the former dominates and that the lattercan even have a detrimental effect.
Bountali Olga: Strategic Double-Booking and Its Impact on Healthcare Operations
Bountali Olga1
1 - Rotman School of Management (Canada)

Abstract:
During the COVID-19 vaccination process, a significant mass of patients booked double(or multiple) appointments for their vaccines with the hope of receiving treatment faster. This led tomany unfulfilled appointments (a.k.a. no-shows) worldwide, left capacity under-utilized, and hindered
the efficiency of the vaccination process during a very crucial period. We introduce a queuing modelwith strategic patients to capture the single- vs. double-booking decisions and examine their impact ofsystem performance and patient outcomes. We use a benchmark representative of transparency, where acentral mechanism allows patients to only single book, and quantify the corresponding loss/gain inducedby double-booking. We further explore potential interventions for central planners and policy makers to
mitigate the negative effects of double-booking.

3.1.3

Invited session W1-3 (Ibrahim): Scheduling in service systems (Room102)

Wednesday 28th, 10:30 - 12:00 - chaired by Rouba Ibrahim
Daw Andrew: Closing the Service: Service Success Heterogeneity and Its Impact on Routing
Castellanos Antonio1 , Daw Andrew2 , Yom-Tov Galit3
1 - Booth School of Business [Chicago] (United States)
2 - University of Southern California (United States)
3 - Technion - Israel Institute of Technology [Haifa] (Israel)

Abstract: Motivated by data from message-based contact centers, we investigate the impact of servicelevel heterogeneity on system-level decision making. In particular, we show that how the service is closed
can have significant effects on real-time routing decisions. Through a static planning problem from theliterature stylized to include salient elements of our context, we prove that while the classic lightest load
policy may be optimal if services are homogenous, service heterogeneity may cause that policy to fail

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even in settings that are otherwise the same. Furthermore, we show that the manner in which services
are closed will either cause or prevent this heterogeneity. Finally, within a class of closure policies thatjustify lightest load routing as optimal, we inspect closure as a decision and determine the optimal closure
policy to balance service success and service duration.
Dong Jing: Optimal routing under demand surge: the value of future arrival rate information
Dong Jing1 , Chen Jinsheng1 , Shi Pengyi2
1 - Columbia University (United States)
2 - Purdue University (United States)

Abstract: With the growing availability of advanced demand forecasting models, an important operations
question is how to utilize this information to make better resource allocation decisions, especially whenfacing demand surges. In this work, we design surge routing policies that explicitly incorporate futurearrival rates for multi-class multi-pool parallel-server systems with partial flexibility. Our proposed policy
is easy to interpret and achieves near-optimal performance. The analysis quantifies the benefit of having
access to future arrival rate information.
Scully Ziv: Performance of the Gittins Policy in the G/G/1 and G/G/k, With and Without
Setup Times
Hong Yige1 , Scully Ziv2
1 - Carnegie Mellon University, Computer Science Department (United States)
2 - Cornell University, Operations Research and Information Engineering (United States)

Abstract: How should we schedule jobs to minimize mean queue length? In the preemptive M/G/1
queue, we know the optimal policy is the Gittins policy, which uses any available information about
jobs’ remaining service times to dynamically prioritize jobs. For models more complex than the M/G/1,
optimal scheduling is generally intractable. This leads us to ask: beyond the M/G/1, does Gittins still
perform well? Recent results indicate that that Gittins performs well in the M/G/k, meaning that its
additive suboptimality gap is bounded by an expression which is negligible in heavy traffic. But allowing
multiple servers is just one way to extend the M/G/1, and most other extensions remain open. Does
Gittins still perform well with non-Poisson arrival processes? Or if servers require setup times whentransitioning from idle to busy? We give the first analysis of the Gittins policy that can handle any
combination of (a) multiple servers, (b) non-Poisson arrivals, and (c) setup times. Our results thus cover
the G/G/1 and G/G/k, with and without setup times, bounding Gittins’s suboptimality gap in eachcase. Each of (a), (b), and (c) adds a term to our bound, but all the terms are negligible in heavy traffic,
thus implying Gittins’s heavy-traffic optimality in all the systems we consider. Another consequence ofour results is that Gittins is optimal in the M/G/1 with setup times at all loads.
Zhong Yueyang: Learning the Scheduling Policy in Time-Varying Multiclass Many Server
Queues with Abandonment
Zhong Yueyang1 , Birge John1 , Ward Amy1
1 - The University of Chicago Booth School of Business (United States)

Abstract: We consider a learning variant of a canonical scheduling problem in a multiclass many server
queue with abandonment (specifically, the Mt /M/N + M and the GI/GI/N + GI queues). The objective
is to minimize the long-run average class-dependent expected linear holding and abandonment costs when
the class-dependent model parameters (arrival rates, service rates and abandonment rates) are a priori
unknown. The difficulty is that even when parameters are known, characterizing an optimal schedulingpolicy appears intractable. Fortunately, the simple cµ/θ rule, that prioritizes classes in accordance with astatic ranking that depends on the costs, the service rates, and the abandonment rates, is asymptotically
optimal as the arrival rates and number of servers become large under certain conditions. Then, our task
is to learn the service and abandonment rates well enough to determine an optimal static priority ranking
for the classes, and we can benchmark our performance by defining the regret relative to the cµ/θ rule.
We propose a Learn-Then-Schedule algorithm, which is composed of a learning phase during which point
estimates of the mean service and patience times are formed, and an exploitation phase during which
the cµ/θ rule with empirical mean estimates as a surrogate for actual parameters is followed. It is shownthat the smallest achievable regret for static priority scheduling policies in T periods is ω(log T ), and we
prove that our proposed algorithm achieves a regret upper bound of O(log T ) which matches the lower
bound.

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3.1.4

35

Invited session W1-4 (Champagnat & Villemonais): Quasi-stationary
distributions in numerical stochastic methods and statistics (Room103)

Wednesday 28th, 10:30 - 12:00 - chaired by Nicolas Champagnat & Denis Villemonais
Corujo Rodriguez Josué: Convergence of the empirical measure induced by a Moran typeparticle system
Corujo Rodriguez Josué1
1 - Institut de Recherche Mathématique Avancée (France)

Abstract: We will discuss some results on the convergence of the empirical distribution induced by amutation-selection Moran type particle system, which is a continuous-time Markov chain. For this model,
it is assumed that the particles can be of different types (among a discrete set) and they interact according
to two mechanisms: a mutation process where they mutate independently of each other according to an
irreducible rate matrix, and a Moran type reproduction process, where two individuals are chosen, one
dies and the other is duplicated. Our results include a uniform in time bound for the convergence of theempirical measure to its deterministic limit, and the asymptotic normality with zero mean and explicit
variance, for the approximation error between the empirical distribution and its limit, when the numberof particles tend towards infinity. We explore the interpretation of this Moran model as a particle process
whose empirical probability measure approximates a quasi-stationary distribution, in the same spirit
as the Fleming-Viot particle systems. This is a work in collaboration with Bertrand Cloez (INRAE
Montpellier).
Fritsch Coralie: Quasi-stationary behavior of the Crump-Young model of chemostat
Cloez Bertrand1 , Fritsch Coralie2,3
1 - INRAE - MISTEA, Montpellier (France)
2 - Institut Élie Cartan de Lorraine (France)
3 - Inria Nancy - Grand Est (France)

Abstract: The Crump-Young model consists of two fully coupled stochastic processes modeling the substrate and micro-organisms dynamics in a chemostat. Substrate evolves following an ordinary differential
equation whose coefficients depend of micro-organisms number. Micro-organisms are modeled though apure jump process whose the jump rates depend on the substrate concentration. It is known that thismodel extincts almost-surely in the sense that micro-organism population vanishes. However, despite
its simplicity, the long-time behavior of this process is not well understood. The existence (but not theuniqueness) of quasi-stationary distribution (QSD) as well as some regularity properties of these QSD
were proved in the literature. Nevertheless, the long-time behavior of the process before extinction was,until now, unknown. In this work, we prove that there exist a unique QSD and that, conditionally on
the non-extinction, the Crump-Young model converges exponentially fast to this QSD.Convergence toquasi-stationary distribution is usually proved though Hilbert techniques. However, our process is notreversible making these techniques difficult to deal with. To overcome this problem, we use recent results [Champagnat-Villemonais 17 & 20, Bansaye-Cloez-Gabriel-Marguet 19] which are a generalizationof usual techniques to prove convergence to stationary distribution.The proof is mainly based on hitting time estimates and Lyapunov functions bounds.Due to the deterministic part, the dynamics of the
Crump-Young model is highly degenerated. The proof then consists of technical sharp estimates.
Strickler Edouard: Wasserstein contraction and convergence of penalized Markov processes
Strickler Edouard1,2 , Villemonais Denis, Champagnat Nicolas
1 - CNRS (France)
2 - Faculté des Sciences et Technologies [Université de Lorraine] (France)

Abstract: We consider a Markov process living in some space E, and killed (penalized) at a rate depending
on its position. In the last decade, several conditions have been given [Champagnat-Villemonais, BansayeCloez-Gabriel-Marguet] ensuring that the law of the process conditioned on survival converges to a quasistationary distribution. When one of these conditions is satisfied, the convergence holds exponentiallyfast in total variation distance. In this talk, we will present very simple examples of penalized Markov
process whose conditional law cannot converge in total variation, and we will give a sufficient condition
implying contraction and convergence of the conditional law in Wasserstein distance. We then apply thiscriterion to a collection of examples.

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Zalduendo Vidal Nicolas: The multi-type bisexual Galton-Watson branching process
Zalduendo Vidal Nicolas1,2 , Fritsch Coralie1,2 , Villemonais Denis1,2
1 - Faculté des Sciences et Technologies [Université de Lorraine] (France)
2 - Inria Nancy - Grand Est (France)

Abstract: The bisexual Galton-Watson process [Daley, 68] is an extension of the classical Galton-Watsonprocess, but taking into account the mating of females and males, which form couples that can accomplish
reproduction. Properties such as extinction conditions and asymptotic behavior have been studied in thepast years, but multi-type versions have only been treated in some particular cases. In this work we deal
with a general multi- dimensional version of Daley’s model, where we consider different types of females
and males, which mate according to a "mating function". We consider that this function is superadditive,
which in simple words implies that two groups of females and males will form a larger number of couples
together rather than separate. One of the main difficulties in the study of this process is the absence of alinear operator that is the key to understand its behavior in the asexual case, but in our case it turns out
to be only concave. To overcome this issue, we use a concave Perron-Frobenius theory [Krause ’94] which
ensures the existence of eigen-elements for some concave operators. Using this tool, we find a necessary
and sufficient condition for almost sure extinction as well as a law of large numbers. Finally, we study
the convergence of the process in the long-time through the identification of a supermartingale.

3.1.5

Invited session W1-5 (Robert): Stochastic Chemical Reaction Networks (Room 104)

Wednesday 28th, 10:30 - 12:00 - chaired by Philippe RobertPopovic Lea: Stationary distributions for reaction networks with discreteness-induced transitions
Popovic Lea1 , Gallinger Cameron1
1 - Concordia University, Montreal (Canada)

Abstract: The phenomenon of discreteness-induced transitions is highly stochastic dependent dynamics
observed in a family of autocatalytic chemical reaction networks. These reaction networks describe thebehaviour of several different species interacting with each other, and the counts of species concentrate indifferent extreme possible values, occasionally switching between them. This phenomenon is only observed
under some regimes of rate parameters in the network, where stochastic effects of small counts of species
takes effect. The dynamics for networks in this family is ergodic with a unique stationary distribution.
While an analytic expression for the stationary distribution in the special case of symmetric autocatalytic
behaviour was derived by Bibbona, Kim, and Wiuf, not much is known about it in the general case. Here
we provide a candidate distribution when the autocatalytic rates are different, inspired by the Moranmodel with genic selections. We show that the this distribution is stationary when autocatalytic rates
are equal, and that it is close to stationary when they are not equal.
Cappelletti Daniele: Two-dimensional weakly reversible reaction networks are positive recurrent
Agazzi Andrea1 , Anderson Dave2 , Cappelletti Daniele3 , Mattingly Jonathan4
1 - Universita‘ degli studi di Pisa (Italy)
2 - University of Wisconsin-Madison (United States)
3 - Politecnico di Torino (Italy)
4 - Duke university (United States)

Abstract: Stochastic reaction networks are continuous-time Markov chains typically used in biology,
epidemiology, and population dynamics. The goal is to keep track of the abundance of the different
reactants over time. What makes them special from a mathematical point of view is the fact that
their qualitative dynamics is described by a finite set of allowed transformation rules, referred to as"reaction graph". A long-standing conjecture is that models with a reaction graph composed by a unionof strongly connected components are necessarily positive recurrent, meaning that each single state is
positive recurrent. In my talk I will discuss why the conjecture makes intuitive sense and why it is difficult
to prove it. I will then show how my collaborators and I adapted Forster-Lyapunov techniques to provethe conjecture in two dimensions.

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Laurence Lucie: Scaling methods for stochastic chemical reaction networks.
Laurence Lucie1
1 - Sorbonne Université-INRIA (France)

Abstract: In this talk we investigate stochastic chemical reaction networks with scaling methods. Thisapproach is used to study the stability properties of the associated Markov processes, but also to investigate the transient behavior of these networks. It may give insight on the impact of complex features ofthese networks such as their polynomial rates,leading to the coexistence of multiple timescales. Several
examples are discussed.
Loeser Eva: Fluid Limit for a Stochastic Model of Enzymatic Processing with General
Distributions
Loeser Eva1 , Williams Ruth1
1 - University of California [San Diego] (United States)

Abstract: In this talk, we consider a stochastic chemical reaction system arising as a model for enzymatic processing in a cell. This can also be thought of as a multiserver multiclass queue with reneging
operating under the random order of service discipline. Stochastic primitives for the model such as production/interarrival times, processing/service times, and lifetimes are assumed to be generally distributed.
We establish a fluid limit for a measure valued process that keeps track of the remaining lifetime for each
entity in the system. We prove uniqueness for fluid model solutions under mild conditions and study theasymptotic behavior of fluid model solutions as time goes to infinity. This talk is based on joint workwith Ruth Williams.

3.1.6

Contributed session W1-6 (Harsha): Markov control and decision
making (Room 105)

Wednesday 28th, 10:30 - 12:00 - chaired by Honnappa Harsha
Chehrazi Naveed: Dynamic Filtration and the Curse of Dimensionality
Chehrazi Naveed1
1 - Washington University in St. Louis (United States)

Abstract:
Many practical optimal control problems suffer from the curse of dimensionality. Using
existing classical methods to reduce the dimensionality of a problem often leads to other complications.
For example, methods that are based on state-space aggregation break the underlying Markovian structureof the problem. Similarly, methods that are based on value function approximation break the HamiltonJacobi-Bellman (HJB) optimality condition. In this work, we show that if instead of aggregating over thestate space we aggregate over the problem’s information filtration, we can reduce the dimensionality of the
problem by an arbitrary degree without comprising its underlying Markovian structure or breaking thevalidity of the HJB equation. We illustrate our approach for an important and open problem of managing
inventory when inventory records are inaccurate. In our formulation of this problem, the inventory level
and its record have separate dynamics. The former is driven by the demand and unobservable loss process
while the latter is driven by the demand and transaction error process. We analytically characterize thedynamics of the retailer’s belief on the shelf inventory level and prove that the optimal restocking policy
is a threshold policy. The presence of transaction errors breaks the retailer’s ability to calculate theshelf inventory distribution by tracking a small set of sufficient statistics: it remains a function of theentire observed history. Hence, computing the optimal restocking policy, even though we obtain its exact
analytical form, remains numerically intractable. We show that by dynamically adjusting the retailer’s
information set, we can reduce the dimensionality of the problem by an arbitrary degree. This effectively
projects the dynamics of the inventory level onto a manifold that is adaptively adjusting to the information
revealed by the inventory record process. We show that the resulting implementable policy, which is
optimal for the chosen dynamic filtration, also exhibits significant robustness to model misspecification.
To our knowledge, this paper is the first paper that uses a dynamically-adjusted information filtration to
tackle the curse of dimensionality.
Mouchtaki Omar: Beyond IID: Data-Driven Decision-Making in Heterogeneous Environments
Besbes Omar1 , Ma Will1 , Mouchtaki Omar1
1 - Columbia University (United States)

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Abstract: How should one leverage historical data when past observations are not perfectly indicativeof the future, e.g., due to the presence of unobserved confounders which one cannot "correct" for?
Motivated by this question, we study a data-driven decision-making framework in which historical samples
are generated from unknown and different distributions assumed to lie in a heterogeneity ball withknown radius and centered around the (also) unknown future (out-of-sample) distribution on which theperformance of a decision will be evaluated. This work aims at analyzing the performance of central
data-driven policies but also near-optimal ones in these heterogeneous environments. We first establish,
for a general class of policies, a new connection between data-driven decision-making and distributionallyrobust optimization with a regret objective. We then leverage this connection to quantify the performancethat is achievable by Sample Average Approximation (SAA) as a function of the radius of the heterogeneity
ball: for any integral probability metric, we derive bounds depending on the approximation parameter, a
notion which quantifies how the interplay between the heterogeneity and the problem structure impacts
the performance of SAA. When SAA is not rate-optimal, we design and analyze problem-dependent
policies achieving rate-optimality. We compare achievable guarantees for three widely-studied problems–newsvendor, pricing, and ski rental– under heterogeneous environments. Our work shows that the typeof achievable performance varies considerably across different combinations of problem classes and notionsof heterogeneity.
Neufeld Ariel: Markov Decision Processes under Model Uncertainty
Neufeld Ariel1
1 - Nanyang Technological University (Singapore)

Abstract: In this talk we introduce a general framework for Markov decision problems under model
uncertainty in a discrete-time infinite horizon setting. By providing a dynamic programming principle
we obtain a local-to-global paradigm, namely solving a local, i.e., a one time-step robust optimization
problem leads to an optimizer of the global (i.e. infinite time-steps) robust stochastic optimal control
problem, as well as to a corresponding worst-case measure. Moreover, we apply this framework to portfolio
optimization involving data of the S&P 500. We present two different types of ambiguity sets; one isfully data-driven given by a Wasserstein-ball around the empirical measure, the second one is describedby a parametric set of multivariate normal distributions, where the corresponding uncertainty sets of theparameters are estimated from the data. It turns out that in scenarios where the market is volatile orbearish, the optimal portfolio strategies from the corresponding robust optimization problem outperforms
the ones without model uncertainty, showcasing the importance of taking model uncertainty into account.
This talk is based on joint work with Julian Sester and Mario Sikic.
Honnappa Harsha: Offline Estimation of Controlled Markov Chains: Minimaxity and Sample Complexity
Honnappa Harsha1 , Banerjee Imon2 , Rao Vinayak1
1 - Department of Statistics, Purdue University (United States)
2 - Department of Statistics, Purdue University (United States)

Abstract: This talk will present our recent advances analyzing nonparametric estimators of transition
kernels of controlled Markov chains, with applications in reinforcement learning. We consider an offline
setting with a fixed dataset, collected using a so-called logging policy. We first consider a discrete controlled Markov chain setting, where the natural nonparametric estimator is a simple counting estimator.
Second, in the setting of continuous state, but discrete action, controlled Markov chains, we consider ahistogram estimator. We develop sample complexity bounds for these estimators and establish conditions
for minimaxity. Our statistical bounds crucially depend on the logging policy through its mixing properties. We show that achieving a particular statistical risk bound involves a subtle and interesting trade-off
between the strength of the mixing properties and the number of samples. We demonstrate the validityof our results under various examples, such as ergodic Markov chains, weakly ergodic inhomogeneous
Markov chains, and controlled Markov chains with non-stationary Markov, episodic, and greedy controls.
Lastly, we use these sample complexity bounds to establish concomitant ones for offline evaluation of
stationary Markov control policies.

3.1.7

Invited session W1-7 (Ben Hamou): Random walks on random graphs(Room 106)

Wednesday 28th, 10:30 - 12:00 - chaired by Anna Ben Hamou

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Nagy Oliver: Linking the mixing times of random walks on static and dynamic randomgraphsNagy Oliver1
1 - Universiteit Leiden (Netherlands)

Abstract: I will present a framework that, subject to mild general conditions, ties together mixing
properties of non-backtracking random walks on top of random graphs generated according to the configuration model in two different settings: in one case the graph does not change in time, whereas in
the other case one works with a dynamic version of the random graph in which the edges are randomly
rewired, but the degrees are preserved. This framework is then used to study an entire class of rewiring
dynamics parametrized by the range of the rewiring relative to the position of the random walk. All thegraph dynamics in this class exhibit the trichotomy we found earlier, namely, no cut-off, one-sided cut-off
or two-sided cut-off. For graph dynamics that interpolate between the two extreme cases in this class ofdynamics, this trichotomy splits further into six different sub-regimes. Joint work with Frank den Hollander, Luca Avena, Hakan Guldas (all three Leiden U.), and Remco van der Hofstad (TU Eindhoven).
These results were published in https://doi.org/10.1016/j.spa.2022.07.009.
Stephan Ludovic: Tensor completion via nomadic walks
Stephan Ludovic1 , Zhu Yizhe2
1 - Ecole Polytechnique Fédérale de Lausanne (Switzerland)
2 - University of California [Irvine] (United States)

Abstract: We study the problem of tensor completion, in which the goal is to recover a tensor T froma randomly sampled subset of its entries. In a very sparse regime, corresponding to the conjectured
algorithmic phase transition, we relate this problem to the study of a nomadic walk matrix, that counts
weighted walks on a random bipartite Erdos-Rényi graph. We precisely characterize the spectrum of thismatrix, and show how its top eigenvectors relate to specific quantities of interest of T. This also yields
provable guarantees of reconstruction in a sparse regime than previous works.
Ben-Hamou Anna: Mixing time of the non-backtracking random walk on two-community
random graphs
Ben-Hamou Anna1
1 - Sorbonne Université - UFR de Mathématiques (France)

Abstract: In this talk, we consider the non-backtracking random walk on sparse random graphs generated
according to a variant of the classical configuration model which incorporates a two-community structure.
The strength of the bottleneck is measured by a parameter which roughly corresponds to the fractionof edges that go from one community to the other. We establish a threshold on this parameter that
separates two different regimes for the mixing time of the walk: a regime with slow mixing without cutoff
and a regime with fast mixing and cutoff.
Sarkovic Andjela: Cutoff for random walk on random graphs with a community structure
Hermon Jonathan1 , Šarković Anđela2 , Sousi Perla2
1 - University of British Columbia (Canada)
2 - University of Cambridge (United Kingdom)

Abstract: We consider a variant of the configuration model with an embedded community structure,
where every vertex has an internal and an outgoing number of half edges. We pick a uniform matchingof the half edges subject to the constraints that internal edges in each community are matched to eachother and the proportion of half edges between communities i and j being Q(i,j). We prove that a simple
random walk on the resulting graph G=(V,E) exhibits cutoff if and only if the product of the Cheeger
constant of Q times log|V| diverges.

3.1.8

Invited session W1-8 (Adan):(Room 202)

Random walks and product forms

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40

Boucherie Richard: Blocking policies for networks of finite capacity quasi-reversible nodes
Boucherie Richard1
1 - Mathematics of Operations Research [University of Twente] (Netherlands)

Abstract: We consider a network of multiple nodes. Customers of different types arrive according toPoisson processes and follow a fixed type-dependent route along some of the nodes. We consider nodes
with finite capacity under two blocking policies: the jump-over protocol, under which an arriving customerthat finds all capacity occupied jumps over the nodes to the next node on its route, and the push-out
protocol under which a customer that finds all capacity occupied pushes a customer in service out of thenode and this customer immediately continues its route to the next node on its route. We show that anode that is quasi-reversible for unlimited capacity remains quasi-reversible for finite capacity.
Dimitriou Ioannis: On some reflected autoregressive processes with dependencies
Dimitriou Ioannis1
1 - University of Ioannina, Department of Mathematics (Greece)

Abstract: Motivated by queueing applications, we study various reflected autoregressive processes withdependencies. Specifically, we study cases where the interarrival and service times are proportionallydependent with/without additive delay, as well as cases cases where interarrival times depends on whether
the service duration of the previous arrival exceeds or not a threshold. We provide expressions for the
Laplace transform of the waiting time of a customer in the system in terms of an infinite product ofknown Laplace transforms. We also study an integer-valued reflected autoregressive process that can beused to model a novel retrial queueing system with orbital searching time to depend on whether the last
busy period starts with an empty or a non empty orbit queue. For such a model the generating functionof the stationary orbit queue length is given as an infinite product of known generating functions. Some
additional generalizations are also discussed.
Walraevens Joris: A discrete-time random walk with jumps in the quarter plane motivatedby road traffic
Walraevens Joris1 , Sasaninejad Sara1 , Wittevrongel Sabine1
1 - Ghent University (Belgium)

Abstract: We analyze a particular two-dimensional process that is motivated by road traffic. Supposethat vehicles arrive to a bottleneck where they have to queue. We assume two types of vehicles, say
regular and freight vehicles. The queue then typically dissolves at a lower speed if freight vehicles arepresent, due to the speed and acceleration characteristics of these vehicles. We translate this to a queueing
model as follows: we have customers (the vehicles) of two different classes arriving in a infinite-capacity
single-server queueing system. When no customers of the second class (freight vehicles) are present at
the beginning of a service time, the service time follows a particular distribution. On the other hand, if
at least one customer of that second class is present, the service time follows another distribution. The
position of the first customer of the second class (if any) together with the total number of customers
behind this customer at departure times forms a two-dimensional Markov chain in the quarter plane. We
construct and solve the functional equation of the joint probability generating function of the stationary
vector of this process. The end solution consists of infinite sums of infinite products, which is a common
result for this type of processes.
Adan Ivo: On the Generating Function Approach for the Analysis of Queueing Models
Adan Ivo1 , Down Doug2 , Van Eijndhoven Stef
1 - Eindhoven University of Technology (Netherlands)
2 - McMaster University (Canada)

Abstract: The stationary analysis of queueing models is often studied through functional equations thatthe probability generating function satisfies. It is commonly argued that the solution of these equations
must be unique. In this talk, we suggest that one must be cautious in making such a conclusion.

3.1.9

Invited session W1-9 (Coculescu): Risk measures in finance and insurance (Room 203)

Wednesday 28th, 10:30 - 12:00 - chaired by Delia Coculescu

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Bignozzi Valeria: Fair valuation under parameter uncertainty
Bignozzi Valeria1 , Tsanakas Andreas, Scognamiglio Salvatore
1 - University of Milano-Bicocca (Italy)

Abstract: Fair valuation of insurance liabilities is generally performed via a two-step approach, combining
quadratic hedging with application of a risk measure on the residual liability, to obtain a cost-of-capitalmargin. This procedure requires the knowledge of the joint distribution of the liability and the assets
in the market, which is often not available in practice and estimated from historical data. Focusing ona framework where we only have uncertainty about the parameters of the distribution we propose toconsider the estimated parameters as random variables themselves and present a bootstrapping approachto evaluate this extra source of risk. This approach is implemented using a fairly general neural network
algorithm.
Grigorova Miryana: Optimal stopping: Bermudan strategies meet non-linear evaluations
Grigorova Miryana1 , Quenez Marie-Claire2 , Yuan Peng1
1 - University of Warwick (United Kingdom)
2 - Université Paris Cité - UFR Mathématiques [Sciences] (France)

Abstract: We address an optimal stopping problem over the set of Bermudan-type strategies (which weunderstand in a more general sense than the stopping strategies for Bermudan options in finance) and with
non-linear (possibly non-convex) operators (non-linear evaluations) assessing the rewards, under generalassumptions on the non-linear operators. This problem can be seen as a problem of risk minimization
with general dynamic (possibly non-convex) risk measures over the set of Bermudan stopping times. We
provide a characterization of the value family V in terms of a suitably defined non-linear Snell envelopeof the the pay-off family. We establish a Dynamic Programming Principle. We provide an optimality
criterion in terms of a non-linear martingale property of V on a stochastic interval. We investigate thenon-linear martingale structure and we show that, under suitable conditions, the first time when the value
family coincides with the pay-off family is optimal. The reasoning simplifies in the case where there is a
finite number, say n, of pre-described stopping times, where n does not depend on the scenario/state ofnature. We provide examples of non-linear operators from the stochastic control, mathematical finance,and risk measurement literature, which enter into our framework. Based on a joint work with Marie-Claire
Quenez (Paris) and Peng Yuan (Warwick).
Hillairet Caroline: Hawkes processes and application to cyber-insurance derivatives
Hillairet Caroline1
1 - ENSAE Paris, CREST (France)

Abstract: In this talk, we provide an expansion formula for the valuation of reinsurance contracts (suchthat Stop-Loss contracts) whose payoff depends on a cumulative loss indexed by a Hawkes process. Thismethodology can be applied to cyber-insurance contracts, as the times of occurrence of cyber-claims
exhibit self-exciting behavior. The expansion formula involves the addition of jumps at deterministic
times to the Hawkes process in the spirit of the well-known integration by parts formula for Poisson
functional. From the actuarial point of view, these processes can be seen as “stressed” scenarios. Basedon Joint works with Anthony Réveillac and Mathieu Rosenbaum.
Rosazza Gianin Emanuela: Capital allocation rules and generalized collapse to the mean
Rosazza Gianin Emanuela1 , Centrone Francesca2
1 - University of Milano-Bicocca (Italy)
2 - Università del Piemonte Orientale (Italy)

Abstract: In the context of risk measures, a key problem consists in deciding how to share the capital
to be allocated among different business lines (or sub-units) of a risky activity. This problem implies
the definition and the axiomatic study of capital allocation rules (CARs) induced by risk measure. See
Denault (2001) and Kalkbrener (2005), among others. A well-known capital allocation principle is theso-called Gradient allocation, whose importance is also recognized in applications and that, intrinsically,
assumes Gateaux differentiability of the underlying coherent risk measure. In this case, a capital allocation
rule "collapses to a mean,̈ in the sense that it reduces to the expectation w.r.t. the Gateaux derivative.
Under conditions weaker than differentiability, the gradient allocation has been generalized by meansof the subdifferential capital allocation (defined in terms of elements of the subdifferential of the risk
measure), also in the convex and quasi-convex setting. The main goal of this paper is to investigate underCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

42

which conditions a capital allocation rule collapses to a mean or, better, to the subdifferential/gradient
allocation. In particular, in a law invariance and in a general framework we make a set of assumptions
under which a capital allocation rule (with respect to a coherent or (quasi)convex risk measure) reduces
to the trivial expectation allocation w.r.t. a suitable probability measure. Our results extend the wellknown result for Gateaux differentiable risk measures and can be seen as a generalized collapse to themean (involving a probability measures not necessarily coinciding with the one given a priori).

3.1.10Contributed session W1-10 (Lim): Simulation and Inference (Room204)

Wednesday 28th, 10:30 - 12:00 - chaired by Eunji Lim
Dwivedi Raaz: Counterfactual inference for sequential experiments
Dwivedi Raaz1 , Tian Katherine2 , Tomkins Sabina3 , Klasnja Predrag3 , Murphy Susan2 , Shah Devavrat4
1 - Harvard and MIT (United States)
2 - Harvard University (United States)
3 - University of Michigan (United States)
4 - MIT (United States)

Abstract: We consider after-study statistical inference for sequentially designed experiments whereinmultiple units are assigned treatments for multiple time points using treatment policies that adapt overtime. Our goal is to provide inference guarantees for the counterfactual mean at the smallest possible
scale – mean outcome under different treatments for each unit and each time – with minimal assumptions
on the adaptive treatment policy. Without any structural assumptions on the counterfactual means, thischallenging task is infeasible due to more unknowns than observed data points. To make progress, we
introduce a latent factor model over the counterfactual means that serves as a non-parametric generalization of the non-linear mixed effects model and the bilinear latent factor model considered in prior works.
For estimation, we use a non-parametric method, namely a variant of nearest neighbors, and establish
a non-asymptotic high probability error bound for the counterfactual mean for each unit and each time.
Under regularity conditions, this bound leads to asymptotically valid confidence intervals for the counterfactual mean as the number of units and time points grows to infinity. Finally, we also introduce an
improved variant of nearest neighbors, which we call a doubly robust nearest neighbor estimator thatprovides a (near-)quadratic improvement in the error when the policies are non-adaptive.
Bayati Mohsen: Adaptive Experiment Design with Panel Data
Xiong Ruoxuan1 , Athey Susan2 , Bayati Mohsen2 , Imbens Guido2
1 - Emory University [Atlanta, GA] (United States)
2 - Stanford Graduate School of Business [Stanford] (United States)

Abstract: In this talk, we study the design and analysis of experiments conducted on a set of units overmultiple time periods where the starting time of the treatment may vary by unit. The design problem
involves selecting an initial treatment time for each unit in order to most precisely estimate both the
instantaneous and cumulative effects of the treatment. In this talk, we consider an adaptive experimental
design setting, where both the decision to continue the experiment and treatment assignment decisions are
updated after each period’s data is collected. We propose a new algorithm, the Precision-Guided Adaptive
Experiment (PGAE) algorithm, that addresses the challenges at both the design stage and at the stageof estimating treatment effects, ensuring valid post-experiment inference accounting for the adaptive
nature of the design. Using realistic settings, we demonstrate that our proposed solutions can reduce theopportunity cost of the experiments by over 50%, compared to non-adaptive design benchmarks.
Lu Yingdong: An analytical approach to Hamiltonian Monte Carlo
Ghosh Soumyadip1 , Lu Yingdong1 , Nowicki Tomasz1
1 - IBM [Yorktown] (United States)

Abstract: Through an analytical approach, we identify Hamiltonian Monte Carlo method as iterationsof linear operators on function spaces of probability densities. First, this approach enables us to show
the sequence of probability densities produced by HMC algorithms converge in Lp space (weakly when p
is between (1,2) and strongly when p>=2). Then, coupled with probabilistic arguments, we demonstratethat the convergence is geometric (in both Lp spaces and Wasserstein space) for HMC implementations
in Euclidean spaces under strong convexity conditions on the target distributions. Finally, this analytical
approach allows us to design a new algorithm, ADHMC, that can utilize a much larger class of auxiliary
distributions, including asymmetric and multi-mode distributions. In addition to guarantee its converge
in theory, we also demonstrate that ADHMC outperforms its traditional HMC version in practice.

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Lim Eunji: The limiting behavior of isotonic and convex regression estimators when themodel is misspecified
Lim Eunji1
1 - Adelphi University (United States)

Abstract: We study the asymptotic behavior of the least squares estimators when the model is possibly
misspecified. We consider the setting where we wish to estimate an unknown function f ∗ from noisy
observations (X1 , Y1 ), ..., (Xn , Yn ); our estimator is the minimizer of sum of squared errors over a set Gof functions. We provide sufficient conditions on the metric entropy of G, under which our proposed
estimator converges to g ∗ as the number of observations increases to infinity, where g ∗ is the minimizer of||g − f ∗ || = E[g(X) − f ∗ (X)]2 over g in G. As corollaries of our theorem, we establish the consistency of
our estimator as the number of observations increases to infinity when G is the set of monotone functionsor the set of convex functions. We also make a connection between the convergence rate of our estimatorand the metric entropy of G. As special cases of our finding, we compute the convergence rate of ourestimator when G is the set of bounded monotone functions or the set of bounded convex functions.

3.1.11

Contributed session W1-11 (Xie): Optimization, games (Room 205)

Wednesday 28th, 10:30 - 12:00 - chaired by Yao Xie
Keppo Jussi: The Wisdom of Strategically Diverse Crowds
Jia Yanwei1 , Keppo Jussi2 , Satopaa Ville3
1 - Columbia University (United States)
2 - National University of Singapore (Singapore)3 - INSEAD (France)

Abstract: We introduce strategic diversity as a new source of heterogeneity in predictions made bya group of agents. Our model describes agents who have private information and different externalmotivations, ranging between conformity and contrarianism. We find that the agents’ strategic tendencies
become more extreme, i.e., conformists (contrarians) get closer to (deviate away from) the crowd averagewhen the crowd becomes, on average, more conformal. Both a randomly selected individual and thegroup’s consensus perform worse as the level of strategic diversity in the group increases. Therefore,
unlike other types of diversity such as information diversity, strategic diversity is not beneficial in crowd
forecasting. If strategic diversity exists, however, a weakly contrarian crowd leads to the best performancefor both a randomly selected agent and the group’s consensus. In contrast to the simple average, themedian is more robust to the perturbation caused by strategic motives. We also discuss the implicationsof such diversity in information aggregation.
Saha Subhamay: Partially Observable Discrete-time Discounted Markov Games with General Utility
Saha Subhamay1 , Bhabak Arnab1
1 - Indian Institute of Technology, Guwahati (India)

Abstract: In this talk, we will discuss about partially observable zero sum games where the state process
is a discrete time Markov chain. We will consider a general utility function in the optimization criterion.
We will show the existence of value for both finite and infinite horizon games and will also establish
the existence of optimal polices. The main step involves converting the partially observable game into
a completely observable game which also keeps track of the total discounted accumulated reward/cost.
This is a joint work with Arnab Bhabak.
Pal Chandan: Discrete-time zero-sum games for Markov chains with risk-sensitive average
cost criterion
Pal Chandan1 , Ghosh Mrinal K2 , Golui Subrata1 , Pradhan Somnath3
1 - Indian Institute of Technology, Guwahati (India)
2 - Indian Institute of Science (India)
3 - Queen’s University (Canada)

Abstract: We study zero-sum stochastic games for controlled discrete time Markov chains with risksensitive average cost criterion with countable/compact state space and Borel action spaces. The payoff
function is nonnegative and possibly unbounded for countable state space case and for compact stateCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

44

space case it is a real-valued and bounded function. For countable state space case, under a certain
Lyapunov type stability assumption on the dynamics we establish the existence of the value and asaddle point equilibrium. For compact state space case we establish these results without any Lyapunov
type stability assumptions. Using the stochastic representation of the principal eigenfunction of theassociated optimality equation, we completely characterize all possible saddle point strategies in the classof stationary Markov strategies. Also, we present and analyze an illustrative example.
Xie Yao: Sinkhorn distributionally robust optimization with application to robust hypothesis tests
Xie Yao1
1 - Georgia Institute of Technology (United States)

Abstract: We present a study of distributionally robust optimization (DRO) using Sinkhorn divergence, which is a type of Wasserstein distance with entropic regularization. Our work introduces aconvex reformulation of the problem that enables computationally tractable solutions for a wider rangeof loss functions. This approach offers a more generalizable worst-case distribution than the Wassersteindivergence-induced uncertainty set by extending to the entire space. We propose an efficient first-orderalgorithm with a bisection search to solve the dual reformulation while providing performance guarantees.
Furthermore, we show how this methodology can be used to solve robust hypothesis tests, a fundamental
problem in statistics. We provide numerical examples using both synthetic and real data to demonstrate
our approach’s competitive performance and computational efficiency. This is joint work with Jie Wang
and Rui Gao.

3.2

Wednesday 28th, 13:30 - 15:00

3.2.1

Invited session W2-2 (Zhan): Strategic queueing II (Room 101)

Wednesday 28th, 13:30 - 15:00 - chaired by Dongyuan Zhan
Afèche Philipp: Pricing In A Two-sided Market With Time-sensitive Customers And Suppliers
Afèche Philipp1 , Akan Mustafa2
1 - UNIVERSITY OF TORONTO (Canada)
2 - Carnegie Mellon University (United States)

Abstract: We consider a firm that matches stochastically arriving and time-sensitive customers andsuppliers. We characterize and compare the structure and performance of the profit-maximizing andsocially optimal pricing policies.
Zhong Yueyang: Behavior-Aware Queueing: When Strategic Customers Meet StrategicServers
Zhong Yueyang1 , Gopalakrishnan Raga2 , Ward Amy1
1 - The University of Chicago Booth School of Business (United States)
2 - Queen’s University Smith School of Business (Canada)

Abstract: Service system design is often informed by queueing theory. Traditional queueing theory
assumes that customers are indefinitely patient and servers work at constant speeds. That is reasonable
in computer science and manufacturing contexts. However, customers and servers in service systems arepeople, and, in contrast to jobs and machines, systemic incentives created by design decisions influence
their behavior. First, we study the behavior of strategic servers whose choice of work speed depends on
managerial decisions regarding (i) how many servers to staff and how much to pay them, and (ii) whether
and when to turn away customers. We develop a game-theoretic many-server Markovian queueing modelwith a finite or infinite buffer in which the work speeds emerge as the solution to a non-cooperative game.
In an asymptotic regime in which demand becomes large and the utility function becomes concave,
we establish existence, uniqueness, and monotonicity properties of underloaded, critically loaded, andoverloaded equilibria for various regions in the design space. We then extend our model to also incorporate
strategic customers’ joining decisions, which endogenously induce a finite buffer. By comparing equilibria
where strategic individuals maximize their own utility with those that maximize social welfare or net
profit, we characterize the managerial costs of ignoring strategic behavior.

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Zur (Schwarz) Elisheva: Priority queues with arrival rate uncertainty
Zur (Schwarz) Elisheva1 , Oz Binyamin1 , Skir Gregory1
1 - The Hebrew University of Jerusalem (Israel)

Abstract: We study an unobservable single-server queue with arrival-rate uncertainty. Customers arrive
according to a Poisson process with a random arrival rate and decide whether to pay for priority. We
characterize the equilibrium customer behavior under different pricing and information disclosure policies.
The model is analyzed under two different assumptions regarding the customers’ degree of rationality.
The first assumes that customers are fully rational and take into account the RASTA (rate-biased ASTA)
phenomenon. The second assumes that customers are naive and base their decision on the PASTA
phenomenon even though the arrival process is not Poisson.
Dongyuan Zhan: Advance Selling and Upgrading in Priority Queues
Wang Yaolei, Cao Ping, Xie Jingui, Dongyuan Zhan1
1 - UCL (United Kingdom)

Abstract: We study advance selling and upgrading in a priority queue setting that emerges in theamusement park industry. Customers choose to buy the fast-track or regular tickets depending on boththeir heterogeneous waiting costs and the demand/congestion level. At the park entrance, customers canchoose tickets based on the observed congestion levels. Advance ticket purchases are cheaper, but may
pose congestion uncertainty. Upgrading options allow customers to purchase regular tickets in advanceand upgrade on-site if the congestion is high. Our objective is to find the optimal pricing scheme tomaximize revenue. We find that upgrading is not necessary if all customers purchase tickets online, but
it may generate more revenue if there exist some offline customers who cannot purchase in advance.

3.2.2

Invited session W2-3 (Dong): Efficient scheduling and resource allocation (Room 102)

Wednesday 28th, 13:30 - 15:00 - chaired by Jing Dong
Arlotto Alessandro: On the constant regret in dynamic resource allocation problems
Vera Alberto1 , Arlotto Alessandro2 , Gurvich Itai3 , Levin Eli4
1 - Amazon (United States)
2 - Duke university [Durham] (United States)
3 - Northwestern University [Chicago, Ill. USA] (United States)
4 - Yale University [New Haven] (United States)

Abstract: In this talk, I will review and compare recent constant-regret results in dynamic resource
allocation problems. In such problems, requests of different types and with different rewards arrive ineach of T periods and, if served, they consume a certain amount of a subset of available resources. Everytime a request arrives, a decision maker must decide whether to serve that request without the possibilityof looking to the future or of changing past decisions. The goal of the decision maker is to maximize
the sum of the expected rewards of the resources that are served. (Based on joint work with A. Vera, I.
Gurvich and E. Levin.)
Ibrahim Rouba: Size-based scheduling in service systems
Ibrahim Rouba1 , Dong Jing
1 - University College London (School of Management, University College London, 1 Canada Square, London E14 5AB,
U.K. United Kingdom)

Abstract: Size-based scheduling policies, such as shortest-remaining-processing-time (SRPT) and shortestjob-first (SJF), have been extensively studied, for decades, yet almost exclusively in single-server queues
with infinitely patient jobs and under exact job-size information. Motivated by applications to service
systems, such as call centers or healthcare facilities, we analyze the performance of size-based schedulingin multiserver queues with abandonment and inexact job-size information. In particular, we demonstrate that system performance under size-based scheduling with noisy service times is asymptoticallyequivalent, in the many-server heavy-traffic limit, to performance under a two-class priority queue where
customers with short service times (below a threshold) are served without wait, and customers with longservice times (above a threshold) eventually abandon without service.

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Sarhangian Vahid: Dynamic transfer policies for parallel queues
Chan Timothy, Park Jangwon, Sarhangian Vahid1
1 - UNIVERSITY OF TORONTO (Canada)

Abstract: Service systems with multiple locations, such as hospital networks, are often vulnerable todisproportionate congestion caused by spatial mismatches in customer demand and available capacity.
The imbalance in congestion motivates transferring customers between different locations. To gain insights into the structure of optimal transfer policies, we consider a system of parallel single-server queues
and investigate dynamic policies for transferring customers between queues at the beginning of discreteintervals. We consider minimizing the total expected cost incurred over a finite horizon, comprised ofholding costs, as well as variable and fixed transfer costs. By analyzing associated discrete-time fluid control problem, we provide insights on the structure of optimal transfer policies and various cost trade-offs.
The discrete-time setting enables us to capture a key trade-off with “excessive” idleness, which arises dueto the inability to re-distribute customers over the duration of a period. When applied to systems of twoqueues, we recover a natural threshold-type structure akin to state-dependent (s,S) policies in inventory
control. This result states that each queue has a pair of optimal “re-order” and “order-up-to” levels suchthat when one queue falls below its re-order level, it is optimal to transfer just enough customers fromthe other to replenish it to its order-up-to level.
Shi Pengyi: Refined Mean-Field Approximation for Discrete-Time Queues to Improve
Itinerary Completion in Coordinated Care Networks
Shi Pengyi1 , Pan Yang2
1 - Purdue University [West Lafayette] (United States)
2 - ETH Zürich (Switzerland)

Abstract: Coordinated care is a burgeoning model of healthcare delivery that involves comprehensive
diagnosis and treatment planning based on collaboration between multiple co-located medical services.
The care model necessitates an itinerary of patient visits to different services within a care network over thecourse of several days. Achieving a rapid completion of patient diagnosis and treatment plans is critical tocare delivery. We develop a new discrete-time queueing network that incorporates several salient featuresin coordinated care to optimize capacity allocation and improve itinerary completion. We characterize
the itinerary completion time with a doubly-stochastic phase-type distribution and develop a refined
mean-field approximation to tackle the curse of dimensionality. We explicitly quantify the convergence
rate for this approximation as O(1/N ) with N being the system size. Not only is this convergence better
than that proven in prior work, but our approximation shows a significant improvement in performance
prediction accuracy when the system size is small, compared to the conventional (unrefined) mean-fieldapproximation. This accuracy makes our approximation appealing to support decision-making in practice.
Using real data, we show that our approach can efficiently solve networks as large as with 26 stationsof services and significant improvements can be achieved over the historical practice as well as intuitive
rule-of-thumb heuristics. We demonstrate hat appointment allocation is a multifaceted problem and that
ignoring any of those facets can lead to poor performance, highlighting the significance of our integrated
approach.

3.2.3

Invited session W2-4 (Fricker): Mean-field approach for analysis ofcomplex stochastic systems II (Room 103)

Wednesday 28th, 13:30 - 15:00 - chaired by Christine Fricker
Allmeier Sebastian: Bias of Multiscale Mean Field Models and Averaging Methods
Allmeier Sebastian1 , Gast Nicolas1
1 - INRIA Grenoble-Rhône Alpes (France)

Abstract: The mean field approximation is a powerful technique which has been used in many settings tostudy large-scale stochastic systems. In the case of two-timescale systems, the approximation is obtainedby a combination of scaling arguments and the use of the averaging principle. In this talk, I will present
how to analyze the approximation error of this average’ mean field approach for a two-timescale model(X, Y), where the slow component X describes a population of interacting particles which is fully coupledwith a rapidly changing environment Y.
We show that under relatively mild conditions the ’average’ mean-field approximation has a bias oforder O(1/N ), where N is a scaling factor. We also derive a steady state bias correction term that reduces
the error to order O(1/N 2 ). This refined ’average’ mean-field approximation allows the computation of

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47

an accurate approximation even for small scaling factors, i.e., N ≈ 10–50. We illustrate the developed
framework and accuracy results through an application to a random access CSMA model.
Castiel Eyal: Time scale separation in Heavy traffic for Queue Based CSMA
Castiel Eyal1 , Sem Borst2 , Laurent Miclo3 , Florian Simatos4 , Philip Whiting5
1 - Georgia Institute of Technology [Atlanta] (United States)
2 - Eindhoven University of Technology
3 - Toulouse School of Economics
4 - ISAE-SUPAERO
5- Macquarie University

Abstract: In wireless networks, the analysis of the Queue Based CSMA algorithm is a challenging
problem due to the complex interactions between scheduling decisions and the queuing network. Queues
are placed on an interference graph, with activation/deactivation rates adapted as a function of the queue
length at each station. For suitably chosen rates, the schedule and queue lengths operate on different
time scales and the schedule homogenizes towards an equilibrium adapted to the current queue lengths.
This talk will focus on a simple interference graph and present a method to gain explicit error bounds
on the time scale separation and use them to prove a deterministic heavy traffic convergence with an
unusual scaling. We will use time scale separation to establish a state space collapse for queue lengths
and investigate the influence of the idleness due to the distributed nature of the algorithm.
El Masmari Soukaina: The asymptotic behavior of a large-scale storage system with loss,
duplication and admitting policies
El Masmari Soukaina1 , El Kharroubi Ahmed
1 - University Hassan II - Casablanca (Morocco)

Abstract: Nowadays, with the huge volume of data generated everyday, one of the crucial aspects of theresearch interest is to establish effective solutions that guarantee a good data storage management. In
this talk we will present interesting results coming from probabilistic tools and the queueing theory that
aim to study different models associ- ated to the unreliable storage systems with duplication mechanism
and the admitting policy. Resolution of the Skorokhod problem is one of the impor- tant tools used
to solve the stochastic differential equations associated to the models considered, in addition to therenormalisation techniques that gave some suitable scaling serving in obtaining interesting conclusions
on the system behavior. In fact the investigation has first been done on a system assumed to be with
infinite capacity where the explicit expression of the fluid limits associated to the model are obtained thathelps in giving a good statistical performance assured in terms of increasing file reliability, to be followedby some results concerning the case of the storage system with finite capacity [2]. This last exhibits theuse of an averaging principle and a Skorokhod problem in its study [3]. The study of similar storage
systems have been already studied by many authors with quite different models see for example [4], [1],[5], [6], and also see the Santiago Duran’s thesis where the study of such unreliable storage system relieson the mean field theory, see [7] Keywords: Skorokhod problem, Fluid limits, Large-scale storage system.
References [1] Feuillet, M., and Robert, P. , Scaling analysis of a transient stochastic network. Advancesin Applied Probability, 46(2), 516-535 (2014). [2] El Kharroubi, A., El Masmari, S. Fluid limits of aloss storage network, Queueing Syst 101, 137-164 (2022). [3] Kharroubi, A. E., Masmari, S. E. (2023).
On a loss storage network with finite capacity. arXiv preprint arXiv:2302.05237. [4] Philippe Robert.
Stochastic Networks and Queues. Vol. 52. Stochastic Mod- elling and Applied Probability Series. NewYork: Springer, June 2003, pp. xix+398. [5] P.J. Hunt and T.G. Kurtz, title Large loss networks, journal
Stochastic Processes and their Applications, volume 53, year 1994, issn 0304-4149, [6] Wen Sun., Mathieu
Feuillet., Philippe Robert. (2016) Analysis of large unreliable stochastic networks. Annals of Applied
Probability, 26, 2959-3000. [7] Duran, Santiago. duran 2020 resource, Resource allocation with observableand unobservable environments. Universit ÌĄe Paul Sabatier-Toulouse III
Mohamed Hanène: Large scale analysis of a stochastic model for a system with two typesof particles
Mohamed Hanène1 , Fricker Christine, Rigonat Alessia
1 - Université Paris Nanterre - Département de Mathématiques et Informatique (France)

Abstract: This talk deals with the large-scale analysis of a system with M particles moving between
the N nodes of a network according to markovian dynamics, N and M being large with the same order.
Each node has a finite but large capacity cN and is subject to the arrival of another type of particles,
called external particles, which leave the node after service. The external particles are more numerous andCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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compete with the M particles of the system for the capacity of the nodes. This is an original simple model
suited to free-floating car-sharing system dynamics. The remaining capacity seen by the M particles isthen random, with transition rates of order N. Therefore, although the main ingredient is a mean-fieldapproach, a stochastic averaging principle allows to obtain the large-scale behavior of the system. It is
described at different time scales by convergence results, especially the mean-field limit and the explicit
long term limit. It turns out that, for this M-particles system behavior, a phase transition occurs between
a saturated and an unsaturated regime. The critical value depends only on parameters of the networkand the external particles.

3.2.4

Invited session W2-5 (Fraiman):(Room 104)

Algorithms on random graphs

Wednesday 28th, 13:30 - 15:00 - chaired by Nicolas Fraiman
Briend Simon: Finding adam under polynomial connectivity assumption
Briend Simon1,2
1 - Universitat Pompeu Fabra [Barcelona] (Spain)
2 - Université Paris-Saclay (France)

Abstract: We study the problem of root finding in growing random graphs under general hypothesis onconnection probabilities. A simple algorithm is introduced, that runs in polynomial time and that outputs
a set of vertices containing the original vertex with high probability. We present its performance in theparticular case of a "general graph" version of the uniform attachment tree. Unlike in the tree, each newvertex connects to several ancestors (and not only one) chosen uniformly at random. We then explain
why this method can be extended to a wide range of growing graphs models and give other examples ofsuch models.
Perarnau Guillem: Short Synchronizing Words for Random Automata
Chapuy Guillaume1 , Perarnau Guillem2,3
1 - Université Paris Cité (France)
2 - Universitat Politècnica de Catalunya (Spain)
3 - Centre Recerca Matemàtica (Spain)

Abstract: We prove that a random deterministic finite automaton on n states has a synchronizing word oflength O(n1/2 log n) with high probability, confirming conjectures of several authors based on numericalsimulations. The best theoretical result prior to this work was the bound O(n log3 n) given by Cyril
Nicaud. Additionally, our proof provides a quasi-quadratic algorithm to find such synchronizing word.
Skerman Fiona: Is it easier to count communities than find them?
Skerman Fiona1 , Rush Cynthia, Wein Alex, Yang Dana
1 - Uppsala Universitet [Uppsala] (Sweden)

Abstract: Random graph models with planted community structure have been studied extensively inthe literature. For the problems of finding algorithms to both detect and recover community structure,
an interesting landscape of statistical and computational phase transitions has emerged. A naturalunanswered question is: might it be possible to infer properties of the community structure (for instance,
the number and sizes of communities) even in situations where actually finding those communities isbelieved to be computationally hard? We show the answer is no. In particular, we consider certain
hypothesis testing problems between models with different community structures, and we show (in the
low-degree polynomial framework) that testing between two options is as hard as finding the communities.
In addition, our methods give the first computational lower bounds for testing between two different‘planted’ distributions, whereas previous results have considered testing between a planted distribution
and an i.i.d. ‘null’ distribution. Joint work with Cynthia Rush, Alex Wein and Dana Yang.
Stegehuis Clara: Is this network problem hard? Theory vs practice
Stegehuis Clara1
1 - University of Twente [Netherlands] (Netherlands)

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Abstract: While many graph-based problems are in theory NP complete, for many such problems there
exist algorithms that run extremely quickly on large-scale real-world networks. This shows a disparity
between theory and practice: while some theoretical examples exist on which any algorithm can takeextremely long, these graphs do usually not appear in real-world networks. Therefore, it is often possible
to show that these problems can be solved efficiently on random graph with realistic network properties.
In this talk, we focus on algorithms that list all cliques in a graph. We show that this problem can takean exponential time even on many classes of random graphs with realistic network properties. However,here again a disparity between theory and practice arises: while we can show an exponential lower boundon the running time of this problem with a max-clique based algorithm, this lower bound is dominatedby a linear term for all practical purposes.

3.2.5

Invited session W2-6 (Namkoong): Frontiers in sequential learning(Room 105)

Wednesday 28th, 13:30 - 15:00 - chaired by Hongseok Namkoong
Caldentey Rene: Diffusion Approximations for a Class of Sequential Experimentation Problems
Caldentey Rene1 , Araman Victor2
1 - The University of Chicago (United States)
2 - American University of Beirut (Lebanon)

Abstract: We consider a decision maker who must choose an action in order to maximize a reward
function that depends on an unknown parameter. The decision maker can delay taking the action inorder to experiment and gather additional information on the unknown parameter. We model the decision
maker’s problem using a Bayesian sequential experimentation framework and use dynamic programmingand diffusion-asymptotic analysis to solve it. For that, we scale our problem in a way that both theaverage number of experiments that is conducted per unit of time is large and the informativeness of eachindividual experiment is low. Under such regime, we derive a diffusion approximation for the sequential
experimentation problem, which provides a number of important insights about the nature of the problem
and its solution
Kalvit Anand: Diffusion limits of multi-armed bandit experiments under optimism-based
policies
Kalvit Anand1 , Zeevi Assaf1
1 - Columbia University (United States)

Abstract: Our work provides new results on the arm-sampling behavior of the classical UCB family of
optimism-based multi-armed bandit algorithms, leading to several important insights. Among these, it
is shown that arm-sampling rates under UCB are asymptotically deterministic, regardless of the problem
complexity. This discovery facilitates new sharp asymptotic characterizations that among other things,
reveal profound distinctions between UCB and Thompson Sampling such as an “incomplete learning"
phenomenon characteristic of the latter.
Zhang Kelly: Inference after Using RL to Learn Across Users for Digital Interventions
Zhang Kelly1
1 - Harvard University [Cambridge] (United States)

Abstract: Online reinforcement learning (RL) algorithms are a key tool for personalizing decision-making
for digital interventions, as these algorithms can use previously collected data from users to learn andimprove future treatment decisions. We provide methods to perform a variety of statistical analyses using
data collected by RL and other adaptive sampling algorithms for digital interventions. In this work, we
focus on data collected by online RL algorithms that can learn across users, i.e., use the data of multiple
users to learn and inform treatment decisions. This data type is important since highly noisy outcomes(as is the case for digital interventions) mean RL algorithms that learn using the data of multiple users
can significantly reduce noise and learn faster. At the same time, this data type is challenging to develop
inferential theory for because online RL algorithms that combine data across induce dependence between
the collected user data trajectories. We develop a general inferential approach for this non-i.i.d. data
type that allows one to, for example, construct confidence intervals for time-varying treatment effects
and perform off-policy analyses.

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Namkoong Hongseok: Adaptive Experimentation at Scale
Namkoong Hongseok1 , Che Ethan1
1 - Columbia Business School (United States)

Abstract: Standard bandit algorithms that assume continual reallocation of measurement effort arechallenging to implement due to delayed feedback and infrastructural/organizational difficulties. Motivated by practical instances involving a handful of reallocation epochs in which outcomes are measured
in batches, we develop a new adaptive experimentation framework that can flexibly handle any batch
size. Our main observation is that normal approximations universal in statistical inference can also guidethe design of scalable adaptive designs. By deriving an asymptotic sequential experiment, we formulate adynamic program to leverage prior information on average rewards. To (approximately) solve the DP, we
propose a simple iterative planning method, Residual Horizon Optimization, and demonstrate it significantly improves statistical power over standard adaptive policies, even when compared to Bayesian bandit
algorithms (e.g., Thompson sampling) that require full distributional knowledge of individual rewards.
Overall, we expand the scope of adaptive experimentation to settings that are difficult for standard adaptive policies, including problems with a small number of reallocation epochs, low signal-to-noise ratio,
and unknown reward distributions.

3.2.6

Invited session W2-7 (Litvak): Spatial network models (Room 106)

Wednesday 28th, 13:30 - 15:00 - chaired by Nelly Litvak
Bindiganavile Ramadas Vinay Kumar: Community detection on block models with geometric kernels
Bindiganavile Ramadas Vinay Kumar1 , Avrachenkov Konstantin1 , Leskela Lasse2
1 - INRIA Sophia Antipolis (France)
2 - Department of Mathematics [Aalto] (Finland)

Abstract: We consider the community recovery problem on a graph of N nodes where every node has
two independent labels: a location label and a community label. A geometric kernel maps the locationsof pairs of nodes to probabilities. Edges are drawn between pairs of nodes based on their communities
and the value of the kernel. Given the graph so generated along with the location labels, the latent
communities of the nodes is to be inferred. Similar models have been investigated in [1,2]. In this talk,we will look into the fundamental limits for recovering the communities in such models. Further, we
propose a spectral algorithm and characterize its performance in terms of the accuracy of community
recovery. This talk is based on a joint work with K. Avrachenkov (INRIA, Sophia Antipolis) and L.
Leskela (Aalto University, Finland). [1] Abbe, E., Baccelli, F., & Sankararaman, A. (2021). Community
detection on Euclidean random graphs. Information and Inference: A Journal of the IMA, 10(1), 109-160.
[2] Avrachenkov, K., Bobu, A., & Dreveton, M. (2021). Higher-order spectral clustering for geometricgraphs. Journal of Fourier Analysis and Applications, 27(2), 22.
Dreveton Maximilien: Higher-Order Spectral Clustering for Geometric Graphs
Dreveton Maximilien1
1 - EPFL (Switzerland)

Abstract: The present paper is devoted to clustering geometric graphs. While the standard spectralclustering is often not effective for geometric graphs, we present an effective generalization, which wecall higher-order spectral clustering. It resembles in concept the classical spectral clustering method but
uses for partitioning the eigenvector associated with a higher-order eigenvalue. We establish the weak
consistency of this algorithm for a wide class of geometric graphs which we call the Soft Geometric Block
Model. A small adjustment of the algorithm provides strong consistency. We also show that our method
is effective in numerical experiments even for graphs of modest size.
Van Der Hoorn Pim: Convergence of discrete graph curvature for random geometric graphs
Van Der Hoorn Pim1 , Cunningham William, Krioukov Dmitri, Lippner Gabor, Trugenberger Carlo
1 - Eindhoven University of Technology (Netherlands)

Abstract: Curvature is a fundamental geometric characteristic of smooth spaces. In recent years different
notions of curvature have been developed for combinatorial discrete objects such as graphs. However, theconnections between such discrete notions of curvature and their smooth counterparts remain unclear.

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In particular, it is not rigorously known if any notion of graph curvature converges to any traditional
notion of curvature of smooth spaces. In this talk I will address this challenge by considering OllivierRicci curvature on random geometric graphs on smooth and complete Riemannian manifolds. I will
identify proper settings for which the Ollivier-Ricci curvature converges to the Ricci curvature of the
manifold. These results create a bridge between the discrete structure of graphs and continuous geometry
underneath them. The results hold for different notions of graph distances, including the rescaled shortest
path distance, and for different graph densities, with the average degree ranging from nearly logarithmic
to nearly linear in the graph size.
Weedage Lotte: Resource and location sharing in spatial networks
Weedage Lotte1 , Stegehuis Clara1
1 - University of Twente (Netherlands)

Abstract:
Infrastructure sharing is a tool in wireless communications to enhance internet coverageand capacity by increasing redundancy and over-provisioning resources. In this settings, resources ofdifferent operators are shared and their cell towers can be collocated. In earlier work, we showed, basedon simulations, that resource and location sharing benefits the network in terms of throughput andcoverage. However, the underlying mathematical theory is underdeveloped. In this talk, we introduce amathematical model for location sharing where base stations of different operators can be collocated on
the same node. We model cell towers and users as two independent Poisson Point Processess. Every user
connects to all cell towers within a certain radius of that user. To quantify the benefit of location sharing,
we define a measure which resembles the strength of a link. This strength depends on the number ofresources at a cell tower, the degree of this cell tower and the average link distance. We show results on
the performance of this strength under different conditions, and derive an optimal radius in which users
should connect to optimize the link strength.

3.2.7

Invited session W2-8 (Weiss): Applications of Dynamic Matching(Room 202)

Wednesday 28th, 13:30 - 15:00 - chaired by Gideon Weiss
Chen Yilun: Bridging online and offline matching: Simulate-Optimize-Assign-Repeat (SOAR)
Chen Yilun1 , Kanoria Yash2 , Kumar Akshit2 , Zhang Wenxin2
1 - SDS, CUHK Shenzhen (China)
2 - Columbia Business School (United States)

Abstract: We consider a feature-based dynamic matching problem faced by centralized platforms ina highly heterogeneous market. Specifically, a set of heterogeneous supply units, each characterizedby i.i.d. supply feature vector, is available initially. In each period, a customer arrives with an i.i.d.
demand weight vector describing her type, and requests to consume a supply unit. The platform seeksa dynamic matching policy that assigns supply units to customers to maximize the expected average
matching utility. We propose and analyze a simple, simulation-based matching policy, dubbed SimulateOptimize-Assign-Repeat (SOAR). We prove that SOAR enjoys a surprisingly universal (near) optimality
guarantee. Indeed, it achieves the optimal regret scaling under various assumptions on the demandand supply distributions and for different matching utility functions. Our result is premised on a novel
connection between the expected matching utility under SOAR and the offline optimum of the matching
instance, which may be of wider applicability and independent interest. Extensive numerical simulations
support the robustness of the performance of SOAR.
Kohlenberg Angela: The Cost of Impatience in Dynamic Matching: Scaling Laws andOperating Regimes
Kohlenberg Angela1 , Gurvich Itai1
1 - Northwestern University [Evanston] (United States)

Abstract: We study matching queues with abandonment. The simplest of these is the two-sided queuewith servers on one side and customers on the other, both arriving dynamically over time and abandoning
if not matched by the time their patience elapses. We identify non-asymptotic and universal scaling laws
for the matching loss due to abandonment, which we refer to as the "cost of impatience." The scalinglaws characterize the way in which this cost depends on the arrival rates and the (possibly different) meanpatience of servers and customers. Our characterization reveals four operating regimes defined by theCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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relationship between the traffic intensity (heavy or light) and the level of "inventory" that can be held(high or low). Traffic intensity is a measure of customer impatience relative to demand-supply mismatch,while inventory level is, similarly, a relative measure of server impatience. The more patient the servers,
the more "inventory" is held in the system. When the inventory level is low, the abandonment rate isgiven by an insightful expression where only the minimum of the two patience rates appears. Considering
the trade-off between abandonment and costly capacity, we characterize the scaling of the optimal safety
capacity as a function of costs, arrival rates, and patience parameters. Known results for the single-sided
queue with abandonment (the M/M/1 + M queue) are recovered as a special case. In the two-sided
queue, the ability to hold "inventory" of patient servers means that the optimal safety capacity is ofsmaller magnitude than that prescribed in the single-sided queue.
Qian Pengyu: The Competition for Partners in Matching Markets
Qian Pengyu1 , Kanoria Yash2 , Min Seung-Ki3
1 - Purdue University [West Lafayette] (United States)
2 - Columbia University (United States)
3 - Korea Advanced Institute of Science and Technology (South Korea)

Abstract: We study the competition for partners in two-sided matching markets with heterogeneous
agent preferences, with a focus on how the equilibrium outcomes depend on the connectivity in themarket. We model random partially connected markets, with each agent having an average degree din a random (undirected) graph, and a uniformly random preference ranking over their neighbors in
the graph. We formally characterize stable matchings in large markets random with small imbalance
and find a threshold in the connectivity d at log2 n (where n is the number of agents on one side of
the market) which separates a “weak competition” regime, where agents on both sides of the market do
equally well, from a “strong competition” regime, where agents on the short (long) side of the market
enjoy a significant advantage (disadvantage). Numerical simulations confirm and sharpen our theoreticalpredictions, and demonstrate robustness to our assumptions.We leverage our characterizations in two
ways: First, we derive prescriptive insights into how to design the connectivity of the market to trade off
optimally between the average agent welfare achieved and the number of agents who remain unmatchedin the market. For most market primitives, we find that the optimal connectivity should lie in the
weak competition regime or at the threshold between the regimes. Second, our analysis uncovers a newconceptual principle governing whether the short side enjoys a significant advantage in a given matchingmarket, which can moreover be applied as a diagnostic tool given only basic summary statistics for themarket. Counterfactual analyses using data on centralized high school admissions in a major USA city
show the practical value of both our design insights and our diagnostic principle.
Weiss Gideon: On A Conjecture for FCFS Parallel Service Systems
Weiss Gideon1
1 - The University of Haifa (Israel)

Abstract: We consider a service system with several types of customers and several types of servers
subject to a bipartite compatibility graph, under the FCFS policy. The design and staffing of such systems
is complicated by the fact that stability, waiting times, and utilization of servers can only be determined
if the matching rates are known. Thus calculation of these matching rates is of great importance but it isintractable in almost all scenarios. Such matching rates are known for the FCFS binary infinite matchingmodel. It was conjectured (see QS 100 anniversary issue) that if the arrival rates of customers and thenumber of servers of each type are large, matching rates will converge to these infinite matching rates.
This conjecture is supported by simulation results. We will discuss attempts to prove this conjecture,and classify easier and harder cases as generality of the model increases.

3.2.8

Invited session W2-9 (Loisel): Actuarial mathematics and quantitativerisk management (Room 203)

Wednesday 28th, 13:30 - 15:00 - chaired by Stéphane Loisel
Barigou Karim: Gaussian Process-Based Mortality Monitoring using Multivariate Cumulative Sum Procedures
Barigou Karim1 , Loisel Stéphane2 , Salhi Yahia2
1 - Université Laval [Québec] (Canada)
2 - Université Claude Bernard Lyon 1 (France)

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Abstract: This paper presents a multivariate cumulative sum (MCUSUM) procedure to detect changes
in mortality intensity, which plays a critical role in risk management for insurance companies and pension
funds. The MCUSUM algorithm is built on Gaussian process-based non-parametric mortality forecasts
and tracks differences between predicted and realized mortality rates in real-time. Unlike univariate
methods, the MCUSUM accounts for interdependence between age-groups and provides a more comprehensive analysis of mortality trends. The efficiency of the MCUSUM method is demonstrated througha comparison to univariate control charts and a case study of recent mortality data in France, Japan,
Canada, and the USA.
Toyoizumi Hiroshi: Optimal Dividend Strategy with Poisson Jump Revenue and Constant
Negative Running Cost
Toyoizumi Hiroshi1
1 - Department of Applied Mathematics [University of Waseda] (Japan)

Abstract: We discuss the optimal dividend strategy of the business with the cash level fluctuating by the
fixed size revenue of Poisson jumps and constant negative running cost, which is the dual of the classic
Cramer-Lundberg risk model. We show that the value function of this problem is the unique viscosity
solution of the first-order variational inequality with delay, and the barrier strategy is optimal. Iteratively
solving the delayed differential equation, we can obtain the barrier value in this optimal strategy and its
appropriate level of working capital.
Marek Oheim: Risk Class Management in Life Insurance Markets
Marek Oheim1
1 - RPTU Kaiserslautern-Landau (Germany)

Abstract: Grouping customers of different risk types into contracts is important for the stability andthe robustness of an insurance market. In order to analyze the effects of these risk coalitions, we set up
a market model which is inspired by a paper of Sass and Seifried (2014). Afterwards, we provide studies
on the equilibrium premiums of classic life insurance products in monopolistic and competitive insurance
markets. In our model we can show that the market specifications have a high impact on the optimal
choice of different contracts in the insurance market. Regarding markets with capacity constraints serve
as a window for a deeper understanding of the model. Further extensions of the model and its applications
to other insurance types complete the talk.
References:
J. Sass, F.T. Seifried (2014). Insurance markets and unisex tariffs: Is the European Court of Justiceimproving or destroying welfare? Scandinavian Actuarial Journal. 2014, 228-254.
Loisel Stéphane: Quickest detection problem and actuarial applications
Loisel Stéphane1
1 - ISFA, Univ. Lyon 1 (France)

Abstract: In this talk we present theoretical and practical results concerning the quickest detection
problem, concerning abrupt changes in some parameters of dynamic population models. We start withthe case of the abrupt change in the intensity of a Poisson process, then explain how to extend the result
to a birth-and-death process, and then present applications on simulated and real-world datasets.

3.2.9

Invited session W2-10 (Mijatovic): Simulation, Estimation and Monte
Carlo Algorithms for Levy Processes (Room 204)

Wednesday 28th, 13:30 - 15:00 - chaired by Aleks Mijatovic
Mariucci Ester: Nonparametric density estimation of small jumps of Lévy processes
Mariucci Ester1 , Duval Céline2 , Jalal Taher1
1 - Université de Versailles Saint-Quentin-en-Yvelines (France)
2 - Université Lille Nord (France) (France)

Abstract: We consider the problem of estimating the density of the process associated with the small
jumps of a pure jump Lévy process, from discrete observations of one trajectory. We discuss results bothfrom low and high frequency observations, for Lévy processes possibly of infinite variation. In a low
frequency setting, we propose an estimator obtained via a spectral approach which achieves the minimax

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rate with respect to the L2 loss. In a high frequency setting we propose two different estimators, a kernel
estimator and one based on a spectral approach, which achieve the same rate of convergence. This ratedepends on the sampling scheme and on the Blumenthal-Getoor index of the process. Finally, we discuss
the optimality of these results. This is a joint work in collaboration with Céline Duval, and Taher Jalal.
Gonzalez Cazares Jorge: Simulation of the drawdown and its duration in Lévy models via
stick-breaking Gaussian approximation
Gonzalez Cazares Jorge1,2
1 - The Alan Turing Institute (United Kingdom)
2 - Department of Statistics [Warwick] (United Kingdom)

Abstract: We develop a computational method for expected functionals of the drawdown and its durationin exponential Lévy models. It is based on a novel simulation algorithm for the joint law of the state,supremum and time the supremum is attained of the Gaussian approximation of a general Lévy process.
We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications andanalyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carloestimators. Monte Carlo methods for Lévy processes (using Gaussian approximation) have been analysedfor Lipschitz payoffs, in which case the computational complexity of our algorithm is up to two orders ofmagnitude smaller when the jump activity is high. At the core of our approach are bounds on certain
Wasserstein distances, obtained via the novel SBG coupling between a Lévy process and its Gaussian
approximation. Numerical performance, based on the implementation in the dedicated GitHub repository,
exhibits a good agreement with our theoretical bounds. This is joint work with Aleksandar Mijatovic.
Lin Feng: Fast exact simulation of the first passage of a tempered stable subordinator
Lin Feng1
1 - University of Warwick [Coventry] (United Kingdom)

Abstract: In this paper we construct a fast exact simulation algorithm for the simulation of the first passage time, undershoot and overshoot of a tempered stable subordinator over an arbitrary non-increasing
absolutely continuous function. We prove that the running time of our algorithms have finite exponential
moments and provide explicit bounds on their expected running time. These algorithms are applied toobtain Monte Carlo estimators for the price of barrier options and for the solutions of fractional partial
differential equations.
Mijatovic Aleksandar: Geometrically Convergent Simulation of the Extrema of Lévy Processes
Mijatovic Aleksandar1,2
1 - The Alan Turing Institute (United Kingdom)
2 - Department of Statistics [Warwick] (United Kingdom)

Abstract: We develop a novel approximate simulation algorithm for the joint law of the position, therunning supremum and the time of the supremum of a general Levy process at an arbitrary finite time.
We prove that the error decays geometrically in Lp (for any p ≥ 1) as a function of the computationalcost, in contrast with the polynomial decay for the approximations available in the literature. We establish a central limit theorem and construct non-asymptotic and asymptotic confidence intervals for thecorresponding Monte Carlo estimator. We prove that the multilevel Monte Carlo estimator has optimal
computational complexity (i.e. of order ϵ−2 if the mean squared error is at most ϵ2 ) for locally Lipschitz
and barrier-type functions of the triplet and develop an unbiased version of the estimator. We illustrate
the performance of the algorithm with numerical examples. This is joint work with Jorge Gonzalez
Cazares and Geronimo Uribe Bravo.

3.2.10Contributed session W2-11 (Vallois): Health and healthcare modeling (Room 205)

Wednesday 28th, 13:30 - 15:00 - chaired by Pierre Vallois
Wang Zicheng: Clonal Diversity at Cancer Recurrence
Leder Kevin1 , Wang Zicheng1
1 - University of Minnesota (United States)

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Abstract: Despite initial success, many cancer therapies eventually fail due to mutation driven drug
resistance. In this work we are interested in the status of the tumor at the time of cancer recurrence. In
particular we investigate clonal diversity at the time of cancer recurrence, an important factor to consider
when treating recurrent cancers. To model cancer dynamics involving drug-sensitive and drug-resistantcells, we employ a two-type branching process. During treatment, drug-resistant cells develop from drugsensitive cells. We investigate the clonal diversity of drug-resistant cells at the time of cancer recurrence,
defined as the first time the population size of drug-resistant cells exceeds a specified proportion of theinitial population of drug-sensitive cells. We examine two clonal diversity indices: the number of clones
and the Simpson’s Index. We obtain the expectation of these two indices and use them to develop statistics
that estimate model parameters. We then look at these two indices conditioned on early recurrence inthe special case of deterministically decaying sensitive population. We demonstrate that the recurrencetime is a useful indicator of clonal diversity, providing new insights into treating recurrent cancers.
Jacko Peter: Bandit Procedures for Designing Patient-Centric Clinical Trials
Jacko Peter1,2 , Villar Sofia S.3
1 - Lancaster University Management School (United Kingdom)
2 - Berry Consultants (United Kingdom)
3 - MRC Biostatistics Unit, University of Cambridge (United Kingdom)

Abstract: Multi-armed bandit problems (MABPs) are a special type of optimal control problem thathas been studied in the fields of operations research, statistics, machine learning, economics, and others.
It is a framework well suited to model resource allocation under uncertainty in a wide variety of contexts.
The use of bandit models to optimally design clinical trials is one of the typical motivating application,
for designing the so-called patient-centric trials, which would take into account the benefit of the in-trial
patients. Nevertheless, the resulting theory has had little influence on the actual design of clinical trials.
Contrary to similar learning problems arising for instance in digital marketing where interventions can betested on millions of users at negligible cost, clinical trials are about “small data”, as recruiting patientsis remarkably expensive and (in many cases) ethically challenging. Due to the focus on small sizes,
we do not resort to the use of the normal distribution to approximate a binomial distribution which is
a common practice for large samples either “for simplicity” or “for ease of computation”. We evaluate
and compare the performance of a variety of operations research and machine learning procedures forthe finite-horizon MABP, including the traditional and still dominant clinical trial design choice - equal
fixed randomization - and interpret them in the context of designing clinical trials. Our results illustrate
how bandit approaches could offer significant advantages, mainly in terms of allocating more patients tobetter interventions, but still pose important inferential challenges, particularly in terms of their resulting
lower statistical power, potential for bias in estimation and existence of closed-form test distributions or
asymptotic theory. We illustrate some promising modifications to bandit procedures to address powerand bias issues, and we reflect upon the open challenges that remain for an increased uptake of bandit
models in clinical trials. Keywords: bandit problem, dynamic resource allocation, index rules, clinical
trialsDe Turck Koen: Limits of arrival processes into vaccination centers with unpunctual patients.
De Turck Koen1 , Fiems Dieter1
1 - Department of TELIN (Ghent University) (Belgium)

Abstract: We study the arrivals into a vaccination center where the time scale of unpunctuality is large
as compared to the scheduling interval. We show that in the limit, the process converges to a Poissonprocess. This can be shown by either classical techniques based on the characteristic function as well as
by a Chen-Stein approach, where the latter also provides relatively easy to compute error bounds on the
distribution.
We then input the pre-limit arrival process into both a finite as well an infinite server system. The
infinite-server case is well-explained by the Poisson limit of the arrival process, but simulation results
show that the finite-server system in its entirety does not converge to an equivalent finite-server systemwith Poisson inputs and consistently shows considerably better performance. We attempt to shed lighton this surprising result by taking into account the (negative) correlation structure of the pre-limit arrival
process and looking at the large-deviations limits.
Vallois Pierre: Probabilistic Cellular Automata modeling of intercellular interactions in airways: complex pattern formation in patients with Chronic Obstructive Pulmonary Disease
Vallois Pierre1

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1 - Institut Élie Cartan de Lorraine (France)

Abstract: The chronic obstructive pulmonary disease (COPD) is a highly prevalent lung disease, inwhich unusual interactions between fibrocytes and CD8+ T lymphocytes in the peribronchial area could
induce chronic inflammation and tissue remodeling. We considered a probabilistic cellular automata typemodel where the two types of cells follow simple local interaction rules taking into account cell death, proliferation, migration and infiltration. A rigorous mathematical analysis carried out within the frameworkof a streamlined model makes it possible to estimate with precision the parameters of the model using
multiscale experimental data obtained in control and disease conditions. The simulation of the model issimple to be implemented. In simulations, two distinct patterns emerged, which can be analyzed quantitatively. In particular, we show that the change in fibrocyte density in the COPD condition is mainly theconsequence of their infiltration into the lung during exacerbations, suggesting possible explanations forexperimental observations in normal and COPD tissue. Our integrated approach combining probabilistic
cellular automata type model and experimental findings will provide further insights into COPD in futurestudies.

3.3

Wednesday 28th, 15:30 - 17:00

3.3.1

Invited session W3-1 (Dorsman, Righter & Gardner): Load balancing
I (Auditorium)

Wednesday 28th, 15:30 - 17:00 - chaired by Jan-Pieter Dorsman, Rhonda Righter &Kristen Gardner
Comte Céline: Reinforcement Learning in Product-Form Queueing Networks
Comte Céline1 , Jonckheere Matthieu1 , Senen-Cerda Albert2 , Sanders Jaron2
1 - LAAS-CNRS (France)
2 - Eindhoven University of Technology [Eindhoven] (Netherlands)

Abstract: We introduce a reinforcement-learning gradient-based algorithm to optimize performance inproduct-form queueing networks. More specifically, our goal is to maximize the undiscounted return in aqueueing network whose equilibrium distribution is given, up to a multiplicative constant, as a product offunctions that are differentiable with respect to a vector of control parameters. Our main contribution isa novel expression of the gradient of the undiscounted return in the form of a covariance vector for which
we can build an unbiased estimator. This estimator is used to optimize the undiscounted return througha stochastic gradient algorithm. The performance of this algorithm is compared numerically to thatof classical reinforcement-learning methods on several examples, such as load-balancing in multiserver
systems. Our current focus is on (i) proving that this algorithm converges to a (global or local) optimum,and (ii) generalizing our results to other Markov decision processes, provided that we can build an
estimator of the gradient of the logarithm of a stationary measure with respect to the vector of control
parameters. This is a joint ongoing work with Matthieu Jonckheere (LAAS-CNRS), Jaron Sanders(Eindhoven University of Technology), and Albert Senen Cerda (Eindhoven University of Technology).
Cardinaels Ellen: Heavy-Traffic Analysis of Redundancy Systems without Complete Resource Pooling
Cardinaels Ellen1 , Borst Sem1 , Van Leeuwaarden Johan2
1 - Eindhoven University of Technology (Netherlands)
2 - Tilburg University [Tilburg] (Netherlands)

Abstract:
The complete resource pooling (CRP) condition has been a common assumption in theheavy-traffic analysis of parallel-server systems. Systems operating under this condition, even when they
are subject to highly intricate job-server assignment constraints, typically achieve the same asymptotic
performance as a fully flexible single-resource system. In contrast, we examine the asymptotic behavior ofparallel-server systems operating under redundancy scheduling policies beyond the typical CRP condition.
Building on existing product-form distributions, we derive an insightful closed-form expression for thejoint probability generating function of the queue lengths. The heavy-traffic analysis when exploiting thestructure in this generating function reveals that the joint distribution of the scaled queue lengths canasymptotically be written in terms of a linear combination of independent and exponentially distributed
random variables. Hence, even without the CRP condition, a broad range of systems involving job-server
assignment constraints exhibit similar behavior and a partial state space collapse can be observed.

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Anton Elene: Novel coupling techniques for the stationary distribution of redundancy-d
models with general scheduling policies
Anton Elene1,2 , Gardner Kristen3
1 - University of the Basque Country/Euskal Herriko Unibertsitatea (Spain)
2 - Eindhoven University of Technology (Netherlands)
3 - Amherst College (United States)

Abstract: In this talk, we characterize the stationary distribution of redundancy-d systems for a broadfamily of scheduling policies. Redundancy has gained considerable attention as a dispatching paradigm
that promises the potential for significant response time improvements. The redundancy-d policy is ofparticular interest in the theoretical community. Much of the prior work on redundancy-d -assuming
exponentially distributed service times and independent and identically distributed copies- has focusedon either deriving response time results under first-come first-served (FCFS) scheduling or stability conditions under a wider range of scheduling policies including processor sharing (PS) and random order ofservice (ROS). In this talk, we consider the family of scheduling policies that are work-conserving, nonanticipating, non-size based, and non-class based. We introduce a novel stochastic coupling techniquethat modifies the standard coupled sample paths approach by resampling the system state at key moments in time. This approach enables us to obtain, the first proof that all scheduling policies within thefamily under consideration admit the same stationary distribution of the number of jobs in the system.
We further extend our results to the cancel-on-start redundancy model and show that the stationary
distribution of the number of jobs in the system coincides under ROS and FCFS, for a large family ofassignment rules including both assign-longest-idle server (ALIS) and random-assignment-to-idle-server(RAIS).
Gardner Kristen: New directions in product forms
Dorsman Jan-Pieter1 , Gardner Kristen2
1 - University of Amsterdam (Netherlands)
2 - Amherst College (United States)

Abstract: In recent years, due to a surge of interest in redundancy based systems, a number of models
involving different compatibilities between jobs and servers in queueing systems, or between agents andresources in matching systems, have been studied. Under appropriate assumptions, e.g. the service ratefunctions satisfy order-independent conditions, the stationary distributions of many of these seemingly
different systems have been shown to have product forms. While multiple broad frameworks of suchproduct-form models have been proposed, the setup of a framework which captures all such models
remains an open problem.
In this presentation, we closely study this open problem. In doing so, we broaden the space ofknown product form systems by generalizing along several dimensions the recently studied pass-andswap queue, which itself is a generalization of the order-independent queue. We also identify several
important questions that remain open and present a roadmap for future study.

3.3.2

Invited session W3-2 (Burnetas): Strategic queueing III (Room 101)

Wednesday 28th, 15:30 - 17:00 - chaired by Apostolos Burnetas
Burnetas Apostolos: Price and Capacity Competition between a Make-to-Order and a
Make-to-Stock Firm with Strategic Customers
Benioudakis Myron1,2 , Deligiannis Michalis1 , Burnetas Apostolos3 , Liberopoulos George1
1 - University of Thessaly (Greece)
2 - Athens University of Economics and Business (Greece)
3 - National and Kapodistrian University of Athens (Greece)

Abstract: We propose a model of two firms competing in a market of time-sensitive strategic customers.
One firm is a make-to-order producer operating on a first-come-first-served basis, while the other isa make-to-stock supplier with a fixed order cost, negligible replenishment lead time, and immediatedelivery. The products sold by the two firms are substitutes but not identical, and customers choose
which product to buy based on their valuations, selling prices, and estimated waiting time at the maketo-order producer. We consider the competition between the two firms in a stylized model of queueing andinventory control policies under strategic customer behavior. The two firms determine their selling prices
and the production capacity and ordering policies, respectively. We analyze Nash equilibrium strategies
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under equilibrium, and discuss coordination possibilities. A practical application is a market of custommade and ready-made products or new and second-hand products, where the high price and delay inacquiring the custom-made product may drive some customers to buy the less expensive ready-madeproduct.
Liberopoulos George: Price and Capacity Optimization in Service Systems with TimeSensitive, Mixed-Risk Customers
Deligiannis Michalis1 , Benioudakis Myron1,2 , Liberopoulos George1 , Burnetas Apostolos3
1 - Department of Mechanical Engineering, University of Thessaly (Greece)
2 - Athens University of Economics and Business (Greece)
3 - National and Kapodistrian University of Athens (Greece)

Abstract: We study a model of a profit-maximizing firm providing service to time-sensitive customers
who must decide whether to join the queue or not. Customers arrive one at a time and cannot observe
the system state, relying on their expected utility to decide. If they join, they exhibit mixed-risk behavior
towards the total waiting time. They are risk-seekers if the waiting time is below a critical threshold, andrisk-averse if it is above it. This behavior affects the customers’ perception of waiting time and is dueto two opposing forces: impatience linked to the service’s value and frustration related to waiting. Eachjoining customer’s utility is the value of service minus the price and waiting cost per unit of perceived
delay. The firm must determine the service price and/or service capacity to maximize its expected profit
given the customers’ arrival rate. We identify the customers’ symmetric aggregate Nash equilibrium
join/balk strategy, derive the firm’s optimal service price and/or capacity, and perform a sensitivity
analysis of the firm’s performance with respect to the customers’ critical threshold.
Oz Binyamin: Strategic flexibility in service systems
Kerner Yoav1 , Oz Binyamin2 , Shneer Seva3
1 - Ben-Gurion University of the Negev (Israel)
2 - The Hebrew University of Jerusalem (Israel)
3 - Heriot-Watt University [Edinburgh] (United Kingdom)

Abstract: We study a multi-server system. Customers may join any unobservable queue in front of eachserver or join all of them simultaneously. In the latter case, they will be served by any available server
and removed immediately from the all other queues upon service completion. Equilibrium and socially
optimal strategies are studied.
Manou Athanasia: Heterogeneous Strategic Customers in an M/M/1 queue: Price of Ignoring Heterogeneity
Economou Antonis1 , Kanavetas Odysseas2 , Manou Athanasia1
1 - Department of Mathematics, National and Kapodistrian University of Athens (Greece)
2 - Mathematical Institute, Leiden University (Netherlands)

Abstract:
We consider the unobservable M/M/1 queue with strategic customers. We assume that
customers decide whether to join or balk based on their expected utility that depends on the reward
from service, the fee, and the waiting cost. Customers are heterogeneous in their waiting cost rate andtheir reward from service. Specifically, we assume that a customer’s reward from service is increasing
in his/her waiting cost rate. We determine equilibrium customer behavior and calculate the expected
revenue of the administrator and the expected welfare under equilibrium. We also calculate the optimal
fee imposed by a revenue-maximizing administrator. We explore the effect of customer heterogeneity
on the administrator revenue, the social welfare and the optimal fee. Finally, we determine the price ofignoring heterogeneity by comparing the performance of the system when the administrator takes into
account customer heterogeneity with the performance when he/she ignores heterogeneity.

3.3.3

Invited session W3-3 (Maguluri):(Room 102)

Stochastic processing networks

Wednesday 28th, 15:30 - 17:00 - chaired by Siva Theja Maguluri
Borst Sem: Load Balancing with Sparse Dynamic Graphs
Goldsztajn Diego1 , Borst Sem1 , Van Leeuwaarden Johan2
1 - Eindhoven University of Technology (Netherlands)
2 - Tilburg University (Netherlands)

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Abstract: Load balancing algorithms play a crucial role in distributing service requests in large-scaleparallel-resource systems such as data centers and cloud networks. Due to the massive size of thesesystems, implementation complexity of load balancing algorithms has emerged as a critical concern besides
conventional performance metrics such as delay. In this talk we examine novel load balancing algorithms
which use random graphs to guide local load exchanges between queues, focusing on sparse topologies toensure scalability. We establish fluid limits which explicitly depend on the probability law of the randomgraphs through the generating function of its limiting degree distribution. The fluid limits demonstratethat these local algorithms match the performance of their global counterparts when the random graphis resampled at a sufficiently high rate. We further show that performance degrades if queues are isolatedwith a positive probability and that performance improves as the degree distribution becomes moreconcentrated. Note: Based on joint work with Diego Goldsztajn (TU/e) and Johan van Leeuwaarden(Tilburg University).
Dai Jim: Asymptotic steady-state independence for generalized Jackson networks in multiscale heavy traffic
Dai Jim1 , Glynn Peter, Xu Yaosheng
1 - Cornell University [New York] (United States)

Abstract: We prove that under a multi-scale heavy traffic condition, the stationary distribution of thescaled queue length process in any generalized Jackson network has a product-form limit. Each component
in the product-form has an exponential distribution, corresponding to the Brownian approximation of asingle station queue. Each “single station” can be constructed precisely and its parameters have a good
intuitive interpretation. The proof employs a basic adjoint relationship (BAR)-approach that has been
advanced recently by Braverman, Dai, and Miyazawa (2017, 2023). This is a joint work with Peter Glynn
at Stanford and Yaosheng Xu at Amazon.
Jhunjhunwala Prakirt: Heavy Traffic Joint Queue Length Distribution without Resource
Pooling
Jhunjhunwala Prakirt1
1 - Georiga Institute of Technology (United States)

Abstract: This work studies the heavy-traffic joint distribution of queue lengths in a well-known stochastic processing network (SPN), viz., an input-queued switch, operating under the MaxWeight schedulingpolicy. An input-queued switch serves as a representative of SPNs that do not satisfy the so-called
Complete Resource Pooling (CRP) condition, and consequently exhibit a multidimensional State Space
Collapse (SSC). Except in special cases, only the mean queue lengths of such non-CRP system is known
in the literature. In this work, we develop the Transform method to study the input-queued switch.
The key challenge is in solving an implicit functional equation involving the Laplace transform of theheavy-traffic limiting distribution. Under a conjecture on uniqueness of the solution of the functional
equation, we obtain an exact joint distribution of the heavy-traffic limiting queue-lengths in terms of anon-linear combination of i.i.d. exponentials.
Varma Sushil: Power-of-d Choices Load Balancing in the Sub-Halfin Whitt Regime
Varma Sushil Mahavir1 , Castro Francisco2 , Maguluri Siva Theja1
1 - Georgia Institute of Technology [Atlanta] (United States)
2 - UCLA Anderson School of Management (United States)

Abstract:
We consider the load balancing system under Poisson arrivals, exponential services, andhomogeneous servers. Upon arrival, a job is to be routed to one of the servers, where it is queued until
service. We consider the Power-of-d choices routing algorithm, which chooses the queue with minimum
length among d randomly sampled queues. We study this system in the many-server heavy-traffic regime
where the number of servers goes to infinity simultaneously when the load approaches the capacity. In
particular, we consider a sequence of systems with n servers, where the arrival rate of the nth systemis λ = n − n1−γ ) for some γ ∈ (0, 0.5). This is known as the sub-Halfin-Whitt regime. It was shownin [Liu Ying (2020)] that under Power-of-d choices routing with d ≥ nγ log n, the queue length behaves
similarly to that of JSQ, and there are asymptotically zero queueing delays. The focus of this paper is
to characterize the behavior when d is below this threshold. We obtain high probability bounds on thequeue lengths for various values of d and large enough n. In particular, we show that when d grows
polynomially in n, but slower than in [Liu Ying (2020)], i.e., if d is θ(nγ log n)1/m for some integer m > 1,
then the asymptotic queue length is m with high probability. This finite queue length behavior is similar
to that of JSQ in the so-called nondegenerate slowdown regime (where γ = 1). Moreover, if d grows

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polylog in n, i.e. slower than any polynomial, but is at least Ω(log(n)3 ), the queue length blows up to
infinity asymptotically similar to that under JSQ in the so-called super slow down regime (γ > 1). We
obtain these results by deploying the iterative state space collapse approach that was first developed by[Liu Gong Ying (2022)]. We first establish a weak state-space collapse on the queue lengths. We thenbootstrap from this weak collapse to iteratively narrow down the region of the collapse. After enoughsteps, this inductive refinement of the collapse provides the bounds that we seek. These sequences ofcollapses are established using Lyapunov drift arguments.

3.3.4

Invited session W3-4 (Cohen): Mean-Field Limits and their Applications (Room 103)

Wednesday 28th, 15:30 - 17:00 - chaired by Asaf Cohen
Kobeissi Ziad: The tragedy of the commons: a Mean-Field Game approach to the reversalof travelling waves
Kobeissi Ziad1,2
1 - Inria Paris (Sierra team) (France)
2 - Institut Louis Bachelier (France)

Abstract: The goal of this talk is to investigate an instance of the tragedy of the commons in the
optimal management of fisheries. The model we choose is that of a fishes’ population that is governedby a bistable equation and that fishermen harvest. We assume that, when no fishermen are present, thefishes’ population is invading (mathematically, there is an invading travelling front). Is it possible thatfishermen, when acting selfishly, each in his or her own best interest, might lead to a reversal of thetravelling wave and, consequently, to an extinction of the global population? To answer this question,
we model the behaviour of individual fishermen using a Mean Field Game approach, and we show thatthe answer is yes. We then show that, at least in some cases, if the fishermen, instead of acting selfishly,
coordinated, each of them could harvest a greater number of fishes, while still guaranteeing the survivalof the population. Our study is illustrated by several numerical simulations. This is a joint work withIdriss Mazari-Fouquet and Domenec Ruiz-Balet.
Lauriere Mathieu: Deep Backward and Galerkin Methods for Learning Finite State Master
Equations
Lauriere Mathieu1
1 - NYU Shanghai (China)

Abstract: We study two methods designed to efficiently solve high-dimensional PDEs in the setting ofmean field master equations. Such master equations are independently interesting because they fully
characterize the value of a game with a large number of players and have a myriad of applications ineconomics, finance, epidemiology, and more. The first method we explore is the deep backward dynamic
programming (DBDP) method-we derive BSDEs for the master equation along some exploratory process
and use this structure to train neural networks to approximate the master equation solution. Then werevisit the deep Galerkin method (DGM) as applied to the master equation and compare the efficacy of
the two methods.
Yeung Lane Chun: A non-asymptotic perspective on mean field control via log-concavity
Yeung Lane Chun1 , Lacker Daniel1 , Mukherjee Sumit2
1 - Department of Industrial Engineering and Operations Research, Columbia University (United States)
2 - Department of Statistics, Columbia University (United States)

Abstract: We study a class of stochastic control problems in which a large number of players cooperatively
choose their drifts to maximize an expected reward minus a quadratic running cost. For a broad classof potentially asymmetric rewards, we show that there exist approximately optimal controls which aredecentralized, in the sense that each player’s control depends only on its own state and not the states ofthe other players. Moreover, the optimal decentralized controls can be constructed non-asymptotically,
without reference to any mean field limit. These results descend from a broader framework inspired by thetheory of nonlinear large deviations of Chatterjee-Dembo, for which we offer an efficient non-asymptotic
perspective in log-concave settings based on functional inequalities. Joint work with Daniel Lacker andSumit Mukherjee.

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Cohen Asaf: Markovian Equilibria In Ergodic Many-Player Games and Mean-Field Games
Cohen Asaf1 , Zell Ethan1
1 - Department of Mathematics - University of Michigan (United States)

Abstract: We consider a symmetric stochastic game with weak interactions between many players. Timeis continuous, the number of states is finite, and costs are ergodic. We prove the existence of a unique
Nash equilibrium in the game and show that its limiting behavior (as the number of players goes toinfinity) is governed by the unique mean-field equilibrium of the corresponding mean-field game.

3.3.5

Invited session W3-5 (Olvera-Cravioto): Processes on graphs (Room104)

Wednesday 28th, 15:30 - 17:00 - chaired by Mariana Olvera-Cravioto
Britton Tom: Semi-directed networks and their relation to epidemic models
Britton Tom1
1 - Stockholm University (Sweden)

Abstract: First we describe the configuration model for a semi-directed network consisting of both
directed and undirected edges, and some large population properties of such networks. Then we showthat the final outcome of a surprisingly large class of epidemic models can be characterized by suchsemi-directed networks.
Diallo Aoudi Mohamed Habib Aliou: Large graph limit of the matching coverage of local
online algorithms on the Configuration model
Diallo Aoudi Mohamed Habib Aliou1 , Moyal Pascal2 , Robin Vincent1
1 - Laboratoire de Mathématiques Appliquées de Compiègne (France)
2 - Institut Élie Cartan de Lorraine (France)

Abstract: In this work, we study the hydrodynamic limits of various ’local’ online matching algorithms on
random graphs generated by the Configuration Model. By exploiting the Markov structure of a measurevalued process representing the joint construction of the graph together with an epxloration algorithm
that constructs a matching on that graph, we obtain an approximation of the dynamics by a familyof ordinary differential equations, for which a simple function of the solution provides the large-graphlimits of the proportion of matched nodes. We then conduct a comparison of this performance metrics
for various online matching algorithms, and study the sensibility on the degree distribution of the graphunder consideration.
Ramanan Kavita: Large deviations for interacting diffusions on local tree-like graphs
Ramanan Kavita1 , Yasodharan Sarath1 , Chen I-Hsun1 , Salkeld William1
1 - Brown University (United States)

Abstract: We establish large deviation principles for a general class of interacting (reflected) diffusions
on sparse graphs. A key ingredient of the proof, which is of possible independent interest, is a tractablecharacterization of the large deviation rate function for marked locally tree-like graphs, which can beviewed as a generalization of Sanov’s theorem. The talk is based on joint works with I.-H. Chen, W.
Salkeld and S. Yasodharan.
Olvera-Cravioto Mariana: Opinion dynamics on directed complex networks
Olvera-Cravioto Mariana1 , Lin Tzu-Chi1 , Fraiman Nicolas1
1 - University of North Carolina, Chapel Hill (United States)

Abstract: We study a general version of the classical DeGroot-Friedkin model on sparse random digraphs
whose local weak limit is a marked Galton-Watson process. This model has been extensively used in thesocial sciences to study opinion dynamics on social networks. The type of graphs in our analysis include
the directed versions of the Erdos-Renyi graph, the Chung-Lu model, the Norros-Reittu model, andthe configuration model, among others. We show that the stationary distribution of a typical vertex in
the graph converges in distribution to a random variable that can be constructed on the limiting tree.
Furthermore, our approach allows us to provide exact formulas for the mean and variance of the limiting

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stationary distribution, which can be used to characterize the conditions that lead to either “consensus” or“polarization”. In particular, we explain how cognitive biases and the presence of stubborn agents (vertices
that cannot be influenced) have on the evolution of opinions, in some cases promoting polarization andin others reducing it.

3.3.6

Invited session W3-6 (Xu): Experimentation and Learning with
Stochastic Models (Room 105)

Wednesday 28th, 15:30 - 17:00 - chaired by Kuang Xu
Agarwal Anish: On Causal Inference with Temporal and Spatial Spillovers in Panel Data
Agarwal Anish1
1 - Columbia University (United States)

Abstract: Panel data is a ubiquitous setting where one collects multiple measurements over time of acollection of heterogeneous units (e.g., individuals, firms, geographic entities). Two pervasive challenges indoing causal inference in such settings are: (1) temporal spillovers - interventions in the past affect current
outcomes; (2) spatial spillovers - interventions a unit receives affects the outcomes of its neighboring units.
We develop a unified causal framework to tackle these two challenges. We do so by proposing a novel
latent factor model that allows one to share information across units, measurements, and interventions,and incorporates both types of spillovers. The model subsumes linear time-varying dynamical systems
and autoregressive processes as a special case, and previously studied models for spatial spillovers. We
show how a variety of causal parameters are identified and estimated despite unobserved confounding.
Li Hannah: Scaling Up Healthcare Interventions: Insights from Experimentation
Li Hannah1,2
1 - Columbia University (United States)
2 - Massachusetts Institute of Technology (United States)

Abstract: A common concern about social interventions, often found in domains like public health,education, and vocational training, is that the interventions that seem promising in randomized controlled
trials (RCTs) may not perform well when scaled to larger populations. Although many factors contributeto these difficulties in scaling up interventions, in this work we highlight and isolate the effects of one suchfactor: capacity constraints. We consider a case study of a mobile health platform designed to improve
patients’ adherence to treatment protocols. We model the dynamics of this platform as a queueing system
and investigate patient adherence in such a system in (smaller) RCTs versus when the platform is scaledout to a larger population. We show that, due to capacity constraints on the system, the effect observedin an RCT may diminish when servicing a larger population. However, we show that the platform canutilize data from the RCT to inform how to scale to a larger population and still ensure performanceclose to that observed in the RCT.
Li Shuangning: Experimenting under Stochastic Congestion
Li Shuangning1 , Johari Ramesh2 , Wager Stefan2 , Xu Kuang2
1 - Harvard University (United States)
2 - Stanford University (United States)

Abstract: Stochastic congestion, a phenomenon in which a system becomes temporarily overwhelmed byrandom surges in demand, occurs frequently in service applications. While randomized experiments havebeen effective in gaining causal insights and prescribing policy improvements in many domains, using themto study stochastic congestion has proven challenging. This is because congestion can induce interference
between customers in the service system and thus hinder subsequent statistical analysis. In this paper, we
aim at getting tailor-made experimental designs and estimators for the interference induced by stochastic
congestion. In particular, taking a standard queueing system as a benchmark model of congestion, westudy how to conduct randomized experiments in a service system that has a single queue with an outside
option. We study switchback experiments and a local perturbation experiment and propose estimators
based on the experiments to estimate the effect of a system parameter on the average arrival rate. We
establish that the estimator from the local perturbation experiment is asymptotically more accurate
than the estimators from the switchback experiments because it takes advantage of the structure of thequeueing system.

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Xu Kuang: Service Slowdown Detection
Xu Kuang1 , Mendelson Gal2
1 - Stanford University (United States)
2 - Technion (Israel)

Abstract: Resource allocation control mechanisms and statistical analysis play key roles in the operationof dynamic service systems. Control is used to achieve desired performance and analysis is used forstatistical tasks such as fault detection and parameter estimation. While control and analysis have beenstudied separately to a large extent, little is known on the interplay between the two. In this work, wederive principles for performing online detection of server slowdown in dynamic service systems. We showthat the choice of control has a drastic impact on the subsequent statistical analysis. Specifically, we showthat congestion based statistics can fail to detect server slowdown when applied to the data generated bysystems which use popular adaptive control schemes. We propose using the controller’s action data as a
new statistic for analysis and prove its effectiveness.

3.3.7

Invited session W3-7 (Goulart): Random matrices and random tensors
in data analysis and machine learning (Room 106)

Wednesday 28th, 15:30 - 17:00 - chaired by Henrique Goulart
Lebeau Hugo: Truncated HOSVD : An Analysis of a Fast Tensor Approximation
Lebeau Hugo1
1 - Université Grenoble Alpes (France)

Abstract: Tools from random matrix theory allow us to study a tensor spiked model describing the noisy
observation of a low multilinear-rank signal. A precise characterization of the spectral behavior of thetensor unfoldings reveals a phase-transition phenomenon below which no information can be recovered.
This analysis gives the exact performance of the approximation with truncated HOSVD. It paves the way
towards a precise study of low-rank tensor approximation.
Mai Xiaoyi: A high dimensional asymptotic view on learning from large data sets
Mai Xiaoyi1
1 - Dept. Math-Info (France)

Abstract: In modern machine learning, we often encounter data sets with comparably large numbers ofsamples and features. It is argued that classical statistical learning theory is insufficient for explaining
the generalization performance on such large data sets, due to the curse of dimensionality and theoverfitting problem of overparametrized learning models. The approach of high dimensional asymptotic
analyses is motivated by the need of modeling and understanding modern machine learning. To apply thisincreasingly popular approach, we develop a flexible framework based on the leave-one-out perturbation,
notably capable of handling implicit optimization and iterative procedure involved in many learningalgorithms. Our analyses allow for original understandings on fundamental questions such the challengeof high dimensional learning, the role of loss function and the effectiveness of learning with less labels.
Practical improvements are proposed based on the insights gained from these analyses.
Seddik Mohamed El Amine: Asymptotics of Asymmetric Random Tensors
Seddik Mohamed El Amine1
1 - Technology Innovation Institute (United Arab Emirates)

Abstract: The purpose of this talk is to conduct an asymptotic analysis of large asymmetric spiked
tensor models using random matrix theory. The presentation will be divided into two parts. In the firstpart, we will focus on analyzing a rank-one model and discussing its algorithmic implications, specificallyin terms of signal recovery using a polynomial time algorithm. In the second part of the presentation, we
will broaden the analysis to include a low-rank spiked model with non-independent components, throughan analysis of tensor deflation procedures.

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Zhu Yizhe: Overparameterized random feature regression with nearly orthogonal data
Wang Zhichao1 , Zhu Yizhe2
1 - University of California [San Diego] (United States)
2 - University of California [Irvine] (United States)

Abstract: We investigate the properties of random feature ridge regression (RFRR) given by a two-layer
neural network with random Gaussian initialization. We study the non-asymptotic behaviors of the RFRR
with nearly orthogonal deterministic unit-length input data vectors in the overparameterized regime,
where the width of the first layer is much larger than the sample size. Our analysis shows high-probability
non-asymptotic concentration results for the training errors, cross-validations, and generalization errorsof RFRR centered around their respective values for a kernel ridge regression (KRR). This KRR isderived from an expected kernel generated by a nonlinear random feature map. We then approximate
the performance of the KRR by a polynomial kernel matrix obtained from the Hermite polynomial
expansion of the activation function, whose degree only depends on the orthogonality among different
data points. This polynomial kernel determines the asymptotic behavior of the RFRR and the KRR. Our
results hold for a wide variety of activation functions and input data sets that exhibit nearly orthogonal
properties. Based on these approximations, we obtain a lower bound for the generalization error of the
RFRR for a nonlinear student-teacher model.

3.3.8

Invited session W3-8 (Banerjee): Online decision making (Room 202)

Wednesday 28th, 15:30 - 17:00 - chaired by Sid Banerjee
Braun Alexander: Simplified Prophet Inequalities for Combinatorial Auctions
Braun Alexander1
1 - University of Bonn (Germany)

Abstract: In this talk, we consider online resource allocation problems where the input is drawn fromknown probability distributions. First, we focus on the special case of online bipartite max-weight matching: Nodes on one side (a.k.a. items) are available offline. Nodes on the other side (a.k.a. buyers) arrive
one-by-one, incident edge-weights are sampled, and an irrevocable matching decision needs to be made.
For this problem, we discuss a pricing-based algorithm: For every offline node, we set a price upfront
based on the distributions. Then, any arriving online node is matched to maximize surplus given theprices. We give a simplified proof for the existence of static item prices which recover the tight competitive
ratio from a paper of Feldman, Gravin and Lucier from 2015. We will then extend the techniques to moregeneral settings where we can allocate multiple items per round and derive state-of-the-art competitive
ratios for these. Based on joint work with Thomas Kesselheim.
Lomys Niccolò: A Mediator Approach to Mechanism Design with Limited Commitment
Lomys Niccolò1 , Yamashita Takuro2
1 - Center for Studies in Economics and Finance & Università di Napoli Federico II (Italy)
2 - Osaka School of International Public Policy, Osaka University (Japan)

Abstract: We study mechanism design with limited commitment. In each period, a principal offers a“spot” contract to a privately informed agent without committing to future contracts. In contrast to theclassical model with a fixed information structure, we allow for all admissible information structures. We
represent the information structure as a fictitious mediator and re-interpret the model as a mechanism
design problem for the committed mediator. We construct examples to explain why new equilibriumoutcomes can arise when considering general information structures. Next, we apply our approach todurable-good monopoly. In the seller-optimal mechanism, trade dynamics and welfare substantiallydiffer from those in the classical model: the seller offers a discount to the high-valuation buyer in theinitial period, followed by the high surplus-extracting price until an endogenous deadline, when thebuyer’s information is revealed without noise. The Coase conjecture fails. We also discuss unmediatedimplementation of the seller-optimal outcome.
Maggiori Andreas: Online Correlation Clustering
Maggiori Andreas1
1 - EPFL (Switzerland)

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Abstract: We consider the classic correlation clustering objective in fully dynamic vertex streams: At
each time a new vertex is added to or deleted from the stream and the goal is to maintain a solution tocorrelation clustering, i.e.: a partition of the vertex set. Each vertex is connected to each other vertex
with either a + or - edge and the goal is to maintain a partition of the vertices that minimizes the numberof + edges across clusters plus the number of - edges within clusters, at each time throughout the update
sequence. We show that one can maintain an O(1)-approximation with O(polylog(n)) amortized update
time in the sublinear regime (where we can make degree queries and sample neighbors in O(1) time). Prior
to our work, the best known result for vertex streams was that of Behnezhad, Derakhshan, Hajiaghayi
Stein and Sudan [FOCS’19], who showed how to maintain a 3-approximation in O(log 4 n)-update timein edge streams (which implies an O(Dlog 4 n)-update time 3-approximation for vertex streams of max
degree D).
Lykouris Thodoris: Group fairness in dynamic refugee assignment
Lykouris Thodoris1
1 - Massachusetts Institute of Technology (United States)

Abstract: Ensuring that refugees and asylum seekers thrive (e.g., find employment) in their host countries is a profound humanitarian goal, and a primary driver of employment is the geographic location
within a host country to which the refugee or asylum seeker is assigned. Recent research has proposed
and implemented algorithms that assign refugees and asylum seekers to geographic locations in a mannerthat maximizes the average employment across all arriving refugees. While these algorithms can havesubstantial overall positive impact, using data from two industry collaborators we show that the impactof these algorithms can vary widely across key subgroups based on country of origin, age, or educational background. Thus motivated, we develop a simple and interpretable framework for incorporatinggroup fairness into the dynamic refugee assignment problem. In particular, the framework can flexibly
incorporate many existing and future definitions of group fairness from the literature (e.g., minmax,
randomized, and proportionally-optimized within-group). Equipped with our framework, we propose
two bid-price algorithms that maximize overall employment while simultaneously yielding provable groupfairness guarantees. Through extensive numerical experiments using various definitions of group fairness
and real-world data from the U.S. and the Netherlands, we show that our algorithms can yield substantial
improvements in group fairness compared to state-of-the-art algorithms with only small relative decreases(≈ 1%-2%) in global performance.

3.3.9

Invited session W3-9 (Daw & Yom-Tov): Hawkes Processes: New
Models and Theory (Room 203)

Wednesday 28th, 15:30 - 17:00 - chaired by Andrew Daw & Galit Yom-Tov
Chen Xinyun: Diffusion limit and staffing of many-server queues with Hawkes input
Chen Xinyun1 , Gong Kang1
1 - The Chinese University of Hong Kong, Shenzhen (China)

Abstract: To investigate the impact of autocorrelation in the arrival process to the performance ofqueueing systems, we consider a single-class many-server system in which customers arrive according to a
Hawkes process. We propose a new asymptotic regime so that the limiting arrival process still possesses
a nontrivial autocorrelation structure. We derive the diffusion limit of the queue length process under
this regime and obtain the heavy-traffic staffing level that is different from the classic square-root rule.
Daw Andrew: Conditional Uniformity and Hawkes Processes
Daw Andrew1
1 - University of Southern California (United States)

Abstract: Classic results show that the Hawkes self-exciting point process can be viewed as a collection of
temporal clusters, in which exogenously generated initial events give rise to endogenously driven descendant events. This perspective provides the distribution of a cluster’s size through a natural connection
to branching processes, but this is irrespective of time. Insight into the chronology of a Hawkes processcluster has been much more elusive. Here, we employ this cluster perspective and a novel adaptation of
the random time change theorem to establish an analog of the conditional uniformity property enjoyedby Poisson processes. Conditional on the number of epochs in a cluster, we show that the transformed
times are jointly uniform within a particular convex polytope. Furthermore, we find that this polytope

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leads to a surprising connection between these continuous state clusters and parking functions, discrete
objects central in enumerative combinatorics and closely related to Dyck paths on the lattice. In particular, we show that uniformly random parking functions constitute hidden spines within Hawkes processclusters. This yields a decomposition that is valuable both methodologically and practically, which wedemonstrate through application to the popular Markovian Hawkes model and proposal of a flexible andefficient simulation algorithm.
Mandjes Michel: Infinite-server queues with Hawkes input
Mandjes Michel1
1 - University of Amsterdam (Netherlands)

Abstract:
In this talk I’ll discuss an analysis of the number of customers in infinite-server queues with a muktivariate self-exciting (Hawkes) arrival process. It is initially assumed that service requirements are exponentially distributed and that the Hawkes arrival process is of a Markovian nature. I obtain a systemof differential equations that characterizes the joint distribution of the arrival intensity and the numberof customers. Moreover, I provide a recursive procedure that explicitly identifies (transient and stationary) moments. Subsequently, I’ll allow for non-Markovian Hawkes arrival processes and non-exponential
service times. By viewing the Hawkes process as a branching process, I’ll show that the probability
generating function of the number of customers in the system can be expressed in terms of the solutionof a fixed-point equation. I’ll also discuss various asymptotic results: I derive the tail of the distributionof the number of customers for the case that the intensity jumps of the Hawkes process are heavy-tailed,and I also consider a heavy-traffic regime.
Weber Thomas: Estimation of Self-Exciting Point Processes from Time-Censored Data
Schneider Philipp1 , Weber Thomas1
1 - EPFL (Switzerland)

Abstract: Self-exciting point processes, widely used to model arrival phenomena in nature and society,are often difficult to identify. The estimation becomes even more challenging when arrivals are recorded
only as bin counts on a finite partition of the observation interval. In this work, we propose the Recursive
Identification with Sample Correction’ (RISC) algorithm for the estimation of process parameters fromtime-censored data. In every iteration, a synthetic sample path is generated and corrected to matchthe observed bin counts. Then the process parameters update, and a new iteration is performed, tosuccessively approximate the stochastic characteristics of the observed process. In terms of finite-sample
approximation error, the proposed framework performs favorably over extant methods, including a naive
locally uniform sample redistribution. The results of an extensive numerical experiment indicate thatthe reconstruction of an intra-bin history based on the conditional intensity of the process is crucial for
obtaining the optimal performance in terms of estimation error.

3.3.10Invited session W3-10 (Brown): Advances in Experimentation and
Dynamic Models (Room 204)

Wednesday 28th, 15:30 - 17:00 - chaired by David Brown
Haugh Martin: Counterfactual Analysis in Dynamic Latent State Models
Haugh Martin1
1 - Imperial College London, Department of Analytics, Marketing and Operations (United Kingdom)

Abstract: We provide an optimization-based framework to perform counterfactual analysis in a dynamic
model with hidden states. Our framework is grounded in the "abduction, action, and prediction" approachto answer counterfactual queries and handles two key challenges where (1) the states are hidden and (2)the model is dynamic. Recognizing the lack of knowledge on the underlying causal mechanism and thepossibility of infinitely many such mechanisms, we optimize over this space and compute upper and lower
bounds on the counterfactual quantity of interest. Our work brings together ideas from causality, statespace models, simulation, and optimization, and we apply it on a breast cancer case study. To the bestof our knowledge, we are the first to compute lower and upper bounds on a counterfactual query in a
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Miao Sentao: Adaptive Lagrangian Policies for Multi-Warehouse Multi-Store Inventory
System with Lost Sales
Chao Xiuli1 , Jasin Stefanus1 , Miao Sentao2,3
1 - University of Michigan (United States)
2 - McGill University (Canada)
3 - University of Colorado Boulder (United States)

Abstract: We consider the inventory control problem of a Multi-Warehouse Multi-Store (MWMS) systemover a time horizon that the warehouses receive no external replenishment. This problem is prevalent inretail settings, and it is referred to in Jackson (1988) as “what to do until your (external) shipment comesin”. The warehouses are stocked with initial inventories and the stores dynamically replenish inventory
from the warehouses in each period of the planning horizon. Excess demand in each period at any
store is lost. The optimal policy for this problem is complex and state-dependent, and due to the curseof dimensionality, computing the optimal policy using standard dynamic programming is numericallyintractable. Static Lagrangian Base Stock (LaBS for short) policy has been developed for this problem(Miao et al. 2022) and shown to be asymptotically optimal. In this paper, we develop adaptive policies
by dynamically adjusting the control parameters of a vanilla static LaBS policy using realized historicaldemands. We show, both theoretically and numerically, that the adaptive policies significantly improve
the performance of LaBS policy, with the magnitude of improvement characterized by the number of times
the policy can be adjusted. In particular, when the number of adjustment is a logarithm of the lengthof time horizon, the policy is rate-optimal in the sense that the rate of the loss (against the performancebenchmark in terms of the length of the time horizon) matches that of the theoretical lower bound forany policy. Our analysis highlights the importance of a careful policy adjustment scheme to enjoy themost benefit of “pooling effect”.
Zoumpoulis Spyros: A Sample Size Calculation for Training and Certifying Targeting Policies
Simester Duncan1 , Timoshenko Artem2 , Zoumpoulis Spyros3
1 - Sloan School of Management (United States)
2 - Northwestern University [Evanston] (United States)
3 - Institut Européen dádministration des Affaires (France)

Abstract: We propose an approach for determining the sample size required when using an experiment
to train and certify a targeting policy. Calculating the rate at which the performance of a targetingmodel improves with additional training data is a complex problem. We approximate the problem
by grouping customers into segments. This allows us to develop computationally efficient algorithms
that calculate the required sample size using simulations or closed-form analytical approximations. We
consider two problem formulations. The first formulation focuses on training a targeting policy thatsatisfies a predefined performance threshold. The second formulation involves out-of-sample certificationof a trained targeting policy in a statistical test. We illustrate our analysis using data from a luxury
fashion retailer.
Brown David: Sequential Search with Acquisition Uncertainty
Brown David1 , Uru Cagin
1 - Duke university [Durham] (Durham, NC 27708 United States)

Abstract: We study a variation of the classical Pandora’s problem in which a decision-maker (DM)sequentially explores alternatives from a given set and learns their values while trying to acquire the bestalternative. The variations in the model we study are (i) alternatives randomly become unavailable duringexploration and (ii) the DM’s ability to acquire a remaining alternative is uncertain and depends on achosen offer price. Such acquisition uncertainties arise in many applications, including housing search,hiring problems, and e-commerce, but greatly complicate the search problem in that optimal policies
retain all previously explored alternative values as part of the problem state, as opposed to only thehighest explored value as in Pandora’s rule. Our central insight is that despite the complexity that theseacquisition uncertainties create, simple greedy policies based on static sequencing and a single thresholdvalue enjoy strong performance guarantees. We develop such a class of policies and show how to compute
them using a greedy algorithm whose worst-case run-time scales linearly (up to logarithmic factors) in thenumber of alternative types. We show that our policies (a) are asymptotically optimal in high multiplicity
regimes with many alternatives and (b) obtain at least 63.2% of the optimal value under a broad set of
conditions. Extensive numerical examples support this theory: we illustrate our policies on a variationof Pandora’s problem with disappearing alternatives and housing search on models calibrated on data

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from the online brokerage Redfin. In these examples, our policies significantly outperform policies basedon Pandora’s rule.

3.3.11

Contributed session W3-11 (Garcia): Stochastic analysis and estimation (Room 205)

Wednesday 28th, 15:30 - 17:00 - chaired by Celia Garcia
Gerencsér Balázs: Convergence and cutoff analysis for a class of random walks inspired byalmost exchangeable data
Gerencsér Balázs1 , Ottolini Andrea2
1 - Alfréd Rényi Institute of Mathematics (Hungary)
2 - University of Washington (United States)

Abstract: We analyze the convergence rates for a family of auto-regressive Markov chains in a hypercube
depending on a noise parameter c, where at each step a randomly chosen coordinate is replaced by anoisy damped weighted average of the others. The interest in the model comes from the connection witha certain Bayesian scheme introduced by de Finetti in the analysis of partially exchangeable data. We
show that when c gets small (corresponding to a vanishing noise), a cutoff phenomenon occurs. However,
without the damping, we get slower, diffusive mixing with no cutoff. Joint work with Andrea Ottolini.
Levering Nikki: Dynamic Routing in a Road Traffic Network
Levering Nikki, Boon Marko1 , Mandjes Michel, Nunez-Queija Rudesindo
1 - Eindhoven University of Technology (Eindhoven University of Technology Den Dolech 2 5612 AZ Eindhoven
Netherlands)

Abstract: Despite measures to reduce congestion, occurrences of both recurrent and non-recurrent congestion are increasing in frequency, resulting in higher delays in road networks with important economic
implications. Intelligent Transportation Systems (ITS) are able to provide vehicles with information on
non-recurrent events and can therefore be used to reduce the delay in travel times. In this talk, we focus
on a dynamic stochastic shortest path problem: our objective is to minimize the expected travel time ofa vehicle, assuming the vehicle may adapt the chosen route while driving. To this end, we introduce a
Markovian velocity model, based on an environmental background process that tracks both random and(semi-)predictable events affecting the vehicle speeds in a highway network. The resulting continuous
routing model allows for correlation between velocities on the arcs and incorporates both recurrent andnon-recurrent congestion. Routing in the proposed dynamic routing model can then be formulated as asemi-Markov decision process (SMDP). It can be shown that the optimal routing policy can be derived
by the use of dynamic programming (DP), but the explosiveness of the size of the state space makes DP
based algorithms computationally intractable. To overcome this, we present the EDSGER algorithm, aDijkstra-like shortest path algorithm that can be used dynamically with real-time response. We quantify the performance of the algorithms and show how to operationalize this flexible data-driven modelby providing numerical examples that use road network detector data provided by the National Data
Warehouse for Traffic Information in The Netherlands.
Garcia Pareja Celia: Unbiased estimation of Wright-Fisher diffusions: an exact simulation
approach
Garcia Pareja Celia1 , Nobile Fabio1
1 - Département de Mathématiques - EPFL (Switzerland)

Abstract: In this talk I will present an unbiased Monte Carlo maximum likelihood estimator for discretely observed Wright-Fisher diffusions. Our approach is based on exact simulation techniques that areof special interest for diffusion processes defined on a bounded domain, where numerical methods typically fail to remain within the required boundaries. We start by building unbiased maximum likelihood
estimators for scalar diffusions and later present an extension to the multidimensional case. Consistency
results of our proposed estimator are also presented and the performance of our method will be illustrated
through numerical examples.

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3.4

Thursday 29th, 10:30 - 12:00

3.4.1

Invited session T1-1 (Avrachenkov & Ayesta): Reinforcement learning(Auditorium)

Thursday 29th, 10:30 - 12:00 - chaired by Kostia Avrachenkov & Urtzi Ayesta
Borkar Vivek: Q-learning in unknown environments
Borkar Vivek1
1 - Indian Institute of Technology Bombay (India)

Abstract: The talk will describe some analytic results for Q-learning in an unknown but stationary
environment, from a point of view of developing theoretically grounded approximation architectures.
Gaujal Bruno: The Sliding Regret or How to Control Bad Episodes in Reinforcement
Learning
Gaujal Bruno1
1 - Inria Grenoble Alpes (France)

Abstract: In this presentation, we will examine the behavior of episodic reinforcement learning algorithms
and question the cost of using sub-optimal policies for arbitrary long periods of time. This induces our firstidea namely the introduction of a new performance measure of a RL algorithm that is more discriminating
than the regret, that we call the sliding regret. Our second contribution is to present a new episodic
reinforcement learning algorithm whose episodes are based on the performance of the current policy
with respect to the best policy over the current confidence set. This is in contrast with all existing RL
algorithms whose episode lengths are only based on the number of visits to the states. This new algorithm
has O(T 1/2 ) regret as most RL algorithms but enjoys a unique additional property: We show that all
current episodic RL algorithms have asymptotically linear sliding regret for all MDPs while the new one
has a O(log(T )1/2 ) asymptotic sliding regret for generic deterministic MDPs.
Robledo Francisco: A Two-Timescale Approach for Learning Whittle Index in RMABPs:
Tabular and Neural Network Implementations
Robledo Francisco1 , Ayesta Urtzi2 , Avrachenkov Konstantin3 , Borkar Vivek4
1 - University of the Basque Country/Euskal Herriko Unibertsitatea (Spain)
2 - Réseaux, Mobiles, Embarqués, Sans fil, Satellites (France)
3 - Inria Sophia Antipolis - Méditerranée (France)
4 - Indian Institute of Technology (India)

Abstract: In this work, we study the use of a two-time scale approach for learning the Whittle index in
Restless Multi-Armed Bandit Problems (RMABPs). The Whittle index policy is a well-known heuristicthat has demonstrated remarkable performance with guaranteed asymptotic optimality in RMABPs. The
two-timescale approach used in this algorithm consists of a faster timescale for updating the state-action
Q-values and a slower timescale for updating the Whittle indices. Our main theoretical result showsthat our tabular implementation guarantees convergence to the theoretical Whittle indices, while ourneural network implementation is able to extrapolate information from different state-action pairs andlearn the Whittle index policy of larger state-space environments faster. Numerical results show that thisapproach outperforms standard Q-learning algorithms, approximate Q-learning based on neural networks,
and other state-of-the-art algorithms.
Srikant R: Performance Bounds for Policy-Based Average Reward Reinforcement Learning
Algorithms
Srikant R1 , Murthy Yashaswini, Moharrami Mehrdad
1 - Coordinated Science Libratory [University of Illinois] (United States)

Abstract: Many policy-based reinforcement learning (RL) algorithms can be viewed as instantiationsof approximate policy iteration (PI), i.e., where policy improvement and policy evaluation are both performed approximately. In applications where the average reward objective is the meaningful performance
metric, often discounted reward formulations are used with the discount factor being close to 1, which isequivalent to making the expected horizon very large. However, the corresponding theoretical bounds forerror performance scale with the square of the horizon. Thus, even after dividing the total reward by theCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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length of the horizon, the corresponding performance bounds for average reward problems go to infinity.
Therefore, an open problem has been to obtain meaningful performance bounds for approximate PI and
RL algorithms for the average-reward setting. In this paper, we solve this open problem by obtaining thefirst non-trivial error bounds for average-reward MDPs which go to zero in the limit where when policy
evaluation and policy improvement errors go to zero

3.4.2

Invited session T1-2 (Hasenbein): Strategic queueing IV (Room 101)

Thursday 29th, 10:30 - 12:00 - chaired by John Hasenbein
Snitkovsky Ran: Stochastic Approximation of Symmetric Nash Equilibria in Queueing
Games
Snitkovsky Ran1 , Ravner Liron
1 - Coller School of Management, Tel Aviv University (Israel)

Abstract:
We suggest a novel stochastic-approximation algorithm to compute a symmetric Nashequilibrium strategy in a general queueing game with a finite action space. The algorithm involves asingle simulation of the queueing process with dynamic updating of the strategy at regeneration times.
Under mild assumptions on the utility function and on the regenerative structure of the queueing process,the algorithm converges to a symmetric equilibrium strategy almost surely. This yields a powerful toolthat can be used to approximate equilibrium strategies in a broad range of strategic queueing models inwhich direct analysis is impracticable.
Van Eekelen Wouter: Rational customers with limited information joining non-observablequeuesVan Eekelen Wouter1
1 - Tilburg University, TiSEM, Department of Econometrics and Operations Research (Netherlands)

Abstract: Consider a firm that offers delay-prone services to a market of rational, delay-sensitive individuals. The customers in this market value the service, but dislike waiting, and will only use the service
if the net benefit exceeds the waiting costs. As there is no real-time queue information available, they
estimate their expected wait costs based on beliefs or information about the total arrival rate of all potential customers. The firm’s service facility is modeled as an M/M/s queue, with this total arrival rate
as input. An important feature of this model is that the total arrival rate is a random variable of which
only the mean, variance and range are known. Using semi-infinite linear programming and primal-dual
techniques, we establish tight bounds for the expected waiting time in this setting, corresponding to an
arrival rate that takes only two values. The proofs depend crucially on the fact that the expected waiting
time, as a function of the arrival rate, has a convex derivative. We apply the novel tight bounds to thefirm’s pricing problem, in which both the firm and customers have partial knowledge about the total
market size. Our findings provide valuable insights into the optimal maximin pricing strategy for thefirm, which strikes the best balance between profit margin and market share.
Logothetis Dimitrios: Reneging behavior of strategic customers in a system with an unreliable server
Kanavetas Odysseas1 , Logothetis Dimitrios1
1 - Mathematical Institute, Leiden University (Netherlands)

Abstract: In this study, we analyze the reneging behavior of strategic customers in a Markovian queuewith an unreliable server. In the event of a breakdown, the server is replaced with a secondary backup
unit with a diminished service speed. The customers decide initially whether to join or balk and in caseof joining, they continuously face the dilemma whether to stay or to renege. To focus on the effect ofreneging, we also consider the case where reneging is prohibited and the customers only decide whether tojoin or to balk. For these models we derive the equilibrium strategies for the customers and we evaluate
the performance of the system under both scenarios. Moreover, we discuss the effects of the server
repairing procedure and we present numerical experiments that reveal key differences in the customer
strategic behavior.

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Dimitrakopoulos Yiannis: The impact of subcontracting practice on Markovian queues withstrategic customers
Dimitrakopoulos Yiannis1 , Benioudakis Myron2,3 , Zissis Dimitris3
1 - National and Kapodistrian University of Athens (Greece)
2 - University of Thessaly [Volos] (Greece)
3 - Athens University of Economics and Business (Greece)

Abstract: We consider a service system consisting of a provider who offers a service in a make-to-orderfashion to a market of potential customers, and a subcontractor who can be recruited to speed up theservice process. The provider operates a Markovian queue at a standard service rate level, charging
joining customers a fixed fee. At each busy cycle of the system, the provider has the option to recruit
the sub-contractor and switch the implemented service rate to a higher level, offering him a salary. The
sub-contractor in turn decides whether he will accept the provider’s offer to increase the system’s service
rate level, and, also incurs a labor cost per time unit. Arriving customers, who are strategic and timesensitive, are not aware of the number of customers in the system and the induced service policy upon
arrival. They evaluate the received service with a fixed value and incur a cost per unit of time of delay.
Based on their utility maximization, they make individual decisions to join the system or balk, considering
the joining behavior of other customers and the strategic involvement of the subcontractor. We formulate
a sequential non-cooperative game among the provider, the subcontractor, and the customers, where allentities act independently to maximize their individual utility, but they also interact since they take into
account the decisions of the others. The game is solved backward to identify the customers’ equilibrium
joining strategies, and the subcontractor’s and provider’s optimal strategies, as well. Finally, we explore
numerically the impact of subcontracting practice on the performance of the system in equilibrium.

3.4.3

Invited session T1-3 (Castellanos): Queueing Systems with Predictions (Room 102)

Thursday 29th, 10:30 - 12:00 - chaired by Antonio Castellanos
Hu Yue: Prediction-Driven Surge Planning with Application in the Emergency Department
Hu Yue1 , Chan Carri2 , Dong Jing2
1 - The University of Chicago (United States)
2 - Columbia University (United States)

Abstract: Determining emergency department (ED) nurse staffing decisions to balance the quality ofservice and staffing cost can be extremely challenging, especially when there is a high level of uncertainty inpatient demand. Increasing data availability and continuing advancements in predictive analytics provide
an opportunity to mitigate demand uncertainty by utilizing demand forecasts. In this work, we study atwo-stage prediction-driven staffing framework where the prediction models are integrated with the base(made weeks in advance) and surge (made nearly real-time) staffing decisions in the ED. We quantify
the benefit of having the ability to use the more expensive surge staffing and identify the importance ofbalancing demand uncertainty versus demand stochasticity. We also propose a near-optimal two-stage
staffing policy that is straightforward to interpret and implement. Lastly, we develop a unified frameworkthat combines parameter estimation, real-time demand forecasts, and capacity sizing in the ED. Highfidelity simulation experiments for the ED demonstrate that the proposed framework can reduce annual
staffing costs by 11%-16% (2M −3 M) while guaranteeing timely access to care.
Singh Simrita: Feature-Based Priority QueuingSingh Simrita1 , Gurvich Itai, Van Mieghem Jan
1 - Santa Clara University (United States)

Abstract: Traditional queuing theory assumes types are known or perfectly observed, and each type istypically put in its type-specific queue which is prioritized using some version of the celebrated c-mu rule;
we call this type-based queueing. We study feature-based priority queuing where types are not perfectly
observed but are inferred from observed features using a “classifier.” A practically appealing approachcombines an off-the-shelf classifier that predicts the type with type-based priority queueing. We propose
a direct approach that optimizes the classifier to directly predict the priority queue from features.
The explicit modeling of the classifier in the queueing-system design is the novel contribution of thispaper. We present an analytic model to study the optimal queue classification that minimizes queuing
delay costs. We study how the optimal number of priority queues and the assignment of features to queues
changes with the classifier accuracy. We present a numerical study on a real data set of medical images
utilized in digital triage in radiology. We find that, relative to type classification, optimal feature-basedpriority queuing can improve delay costs by up to 54% using state-of-the-art image classifiersCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS72

Yom-Tov Galit: Optimising Hematology Patients LOS Using Mortality and Infection Risk
Predictions
Yom-Tov Galit1 , Armony Mor
1 - Technion - Israel Institute of Technology (Israel)

Abstract: Previous research has shown that early discharge of patients may hurt their medical outcomes.
However, in many cases the “optimal" length of stay (LOS) and the best location for treatment of thepatient are not obvious. A case in point is hematology patients, for whom these are critical decisions.
Patients with hematological malignancies are susceptible to life-threatening infections after chemotherapy. Sending these patients home early minimizes infection risk, while keeping them longer for hospitalobservation minimizes mortality risks if an infection occurs. We develop LOS optimization models forhematology patients that balance the risks of patient infection and mortality. We develop a Markov
decision process formulation to explore the impact of the infection and mortality risk predictions on the
optimal LOS from a single-patient perspective. We further consider the social optimization problem inwhich capacity constraints limit the ability of hospitals to keep patients for the entirety of their optimal
LOS. Using fluid models, we find that the optimal solution takes the form of a two-threshold policy. This
policy may block some patients and immediately route them to home care, or speed up some patients’
LOS and send them to be home-cared early after an observation period at the hospital.
Physicians can use our model to determine a personalised optimal LOS for patients according to theirinfection and mortality risk predictions. Furthermore, they can adjust that decision according to thecurrent hospital load. In a case study, we show that around 75% of the patient population need someobservation period. If the hospital is overloaded, using a speedup only policy is optimal for 90% of thepatient types; applying it to all patient types increases overall mortality risk by 0.5%.
Legros Benjamin: Why rational queueing of loss-averse users undermines the benefits ofadded resources
Legros Benjamin1 , Van Leeuwaarden Johan, Fransoo Jan
1 - Ecole de Management de Normandie (France)

Abstract: While policy makers regularly add capacity to reduce congestion on roads, hospitals, and otherpublic services, empirical studies show that adding resources does not always reduce congestion and wait
times. To clarify this counterintuitive phenomenon, we propose an explanatory model of rational lossaverse users who decide whether or not to access a service. Loss-averse users are more sensitive to time lostthan to time gained. Using a rational queueing approach, we show that the equilibrium wait time increases
monotonically with the number of resources. This insight might render investments in infrastructure suchas roads and hospitals questionable, and creates the dilemma between facilitating more demand by adding
resources and discouraging demand by reducing resources. We offer as explanation that adding resources
reduces wait time variability, so that loss-averse users more readily join. In addition, we show that the
number of resources does not only influence the volume of demand but also its nature: Adding resources
attracts users who are less sensitive to expected wait and more sensitive to wait variability, which implies
that large systems predominantly attract users who prefer reliability over efficiency.

3.4.4

Invited session T1-4 (Baccelli & Foss): Infinite-dimensional queueing/communication systems (Room 103)

Thursday 29th, 10:30 - 12:00 - chaired by François Baccelli & Sergey Foss
Curien Nicolas: Parking problems on random trees I
Curien Nicolas1
1 - Laboratoire de Mathématiques d’Orsay (France)

Abstract: Consider a rooted tree and let cars arrive sequentially, independently, and uniformly on its
vertices. Each car tries to park on its arrival node, and if the spot is already occupied, it drives towards
the root of the tree and parks as soon as possible. If it does not find an empty spot, it exists throughthe root. Recently, a phase transition has been established for this model and we shall survey the recent
exciting developpments about it in connections with the theory of random graphs, coalescent processes
and growth-fragmentation trees.

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Curien Nicolas: Parking problems on random trees II
Curien Nicolas1
1 - Laboratoire de Mathématiques d’Orsay (France)

Abstract: See above.
Sankararaman Abishek: Interference Queueing Networks on Grids
Sankararaman Abishek1
1 - Amazon Web Services [Seattle] (United States)

Abstract: Motivated by problems in wireless network dynamics, we study a countably infinite collectionof interacting queues, with a queue located at each point of the d-dimensional integer grid, having independent Poisson arrivals, but dependent service rates. The service discipline is of the processor sharing type,with the service rate in each queue slowed down, when the neighboring queues have a larger workload.
The interactions are translation invariant in space and is neither of the Jackson Networks type, nor of themean-field type. Coupling and percolation techniques are first used to show that this dynamics has welldefined trajectories. Coupling from the past techniques are then proposed to build its minimal stationary
regime. The rate conservation principle of Palm calculus is then used to identify the stability conditionof this system, where the notion of stability is appropriately defined for an infinite dimensional process.
We show that the identified condition is also necessary in certain special cases and conjecture it to betrue in all cases. Remarkably, the rate conservation principle also provides a closed-form ex- pression forthe mean queue size. When the stability condition holds, this minimal solution is the unique translation
invariant stationary regime. In ad- dition, there exists a range of small initial conditions for which the
dynamics is attracted to the minimal regime. Nevertheless, there exists another range of larger thoughfinite initial conditions for which the dynamics diverges, even though stability criterion holds. We further
discuss progress made on existence of exponential moments for this class of networks using algebraic rate
conservation tools.
Wang Zhe: The Poisson HailModel, Stability, and Power Law Conditions
Mountford Thomas1 , Wang Zhe1
1 - Département de Mathématiques - EPFL (Switzerland)

Abstract: The Poisson hail model is a stochastic system of interacting queues in Zd . Points in Zd
represent servers, which receive jobs according to i.i.d. marked Poisson processes. Each job has random
spatial and temporal sizes (R, T ). Denote by W (x, t) the workload of the system at a space-time point(x, t). The “stability” corresponds to the tightness of the family {W (0, t) : t > 0}. We will discuss powerlaw conditions on the sizes (R, T ) that guarantee “stability.” In particular, we will deal with the case of
infinite speed of propagation and describe a critical curve. This is joint work with Thomas Mountford.

3.4.5

Invited session T1-5 (Coron): Stochastic models for ecology and evolution (Room 104)

Thursday 29th, 10:30 - 12:00 - chaired by Camille Coron
Costa Manon: Eco-evolutionary cycles in a multi-type predator-prey system
Costa Manon1 , Czuppon Pete2 , Forien Raphaël3
1 - Institut de Mathématiques de Toulouse UMR5219 (France)
2 - University of Münster (Germany)
3 - INRAE, Avignon (France)

Abstract: In this talk, we present and study a model for two populations with a predator-prey interaction,
where each population is composed of two types of individuals, denoted by 0 and 1, such that predatorsof a given type thrive in the presence of similar prey of type, while preys of a given type are more likely
to survive in the presence of predators of the different type. We consider a limit in large population
and rare mutations at an intermediate scale, namely the individual mutation rate vanishes while thetotal mutation rate tends to infinity. We prove that depending on the parameters of the model different
scenario can occur: successive prey and predator invasion leading to the coexistence of four types, or
successive invasion of prey in a population of resident predator leading either to prey extinction or to thecoexistence of all types, ...

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Marguet Aline: Spread of parasites affecting death and division rates in a cell population
Marguet Aline1 , Smadi Charline2,3
1 - Inria Grenoble (France)
2 - Laboratoire des EcoSystèmes et des Sociétés en Montagne (France)
3 - Institut Fourier (France)

Abstract: We introduce a general class of branching Markov processes for the modelling of a parasite
infection in a cell population. Each cell contains a quantity of parasites which evolves as a diffusion withpositive jumps. The drift, diffusive function and positive jump rate of this quantity of parasites dependon its current value. The division rate of the cells also depends on the quantity of parasites they contain.
At division, a cell gives birth to two daughter cells and shares its parasites between them. Cells may
also die, at a rate which may depend on the quantity of parasites they contain. We study the long-time
behaviour of the parasite infection and exhibit conditions, on both the parasites and the cell population
dynamics, for the survival of the cell population.
Pfaffelhuber Peter: Population genetic models for transposable elements
Pfaffelhuber Peter1 , Wakolbinger Anton2
1 - University of Freiburg (Germany)
2 - University of Frankfurt (Germany)

Abstract: Consider a population of size N , where each individual carries k = 0, 1, 2, ... transposable elements (TEs). We follow the bi-parental Moran model for reproduction. This means that every individual
dies at rate N/2, and is replaced by a child of two randomly chosen parents. If they have k and l TEs,each TE is inherited with probability 1/2 to the child. Denoting by XkN (t) the frequency of individuals
carrying k TEs at time t, we show a weak limit for X N as N → ∞, and discuss shortcomings of themodel with respect to the underlying dynamics of TEs. Joint work with Anton Wakolbinger (University
of Frankfurt).
Tourniaire Julie: A branching particle system as a model of pushed fronts
Tourniaire Julie1
1 - Institute of Science and Technology [Klosterneuburg, Austria] (Austria)

Abstract: We consider a system of particles performing a one-dimensional dyadic branching Brownian
motion with space-dependent branching rate r(x), negative drift −µ and killed upon reaching 0. Moreprecisely, the particles branch at rate ρ/2 in [0, 1], for some ρ ≥ 1, and at rate 1/2 in (1, +∞). The drift
is chosen in such a way that the system is critical. This system can be seen as an analytically tractablemodel for fluctuating fronts, describing the internal mechanisms driving the invasion of a habitat by acooperating population. Recent studies from Birzu, Hallatschek and Korolev on the noisy FKPP equation
with Allee effect suggest the existence of three classes of fluctuating fronts: pulled, semi pushed and fully
pushed fronts. In this talk, we will show that the particle system exhibits the same phase transitions as
the SPDE, suggesting the existence of a universality class.

3.4.6

Invited session T1-6 (Shi): Learning algorithms for managing service
systems (Room 105)

Thursday 29th, 10:30 - 12:00 - chaired by Pengyi Shi
Bastani Hamsa: Decision-Aware Learning for Global Health Supply Chains
Chung Angel1 , Rostami Vahid2 , Bastani Hamsa1 , Bastani Osbert3
1 - The Wharton School (United States)
2 - Macro-Eyes (United States)
3 - School of Engineering and Applied Science [University of Pennsylvania] (United States)

Abstract: The combination of machine learning (for prediction) and optimization (for decision-making)is increasingly used in practice. However, a key challenge is the need to align the loss function used totrain the machine learning model with the decision loss associated with the downstream optimization
problem. Traditional solutions have limited flexibility in the model architecture and/or scale poorly tolarge datasets. We propose a principled decision-aware learning algorithm that uses a novel Taylor expansion of the optimal decision loss to derive the machine learning loss. Importantly, our approach only
requires a simple re-weighting of the training data, allowing it to flexibly and scalably be incorporated

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into complex modern data science pipelines, yet producing sizable efficiency gains. We apply our framework to optimize the distribution of essential medicines in collaboration with policymakers at the Sierra
Leone National Medical Supplies Agency; highly uncertain demand and limited budgets currently resultin excessive unmet demand. We leverage random forests with meta-learning to learn complex crosscorrelations across facilities, and apply our decision-aware learning approach to align the prediction loss
with the objective of minimizing unmet demand. Out-of-sample results demonstrate that our end-to-end
approach significantly reduces unmet demand across 1000+ health facilities throughout Sierra Leone.
Che Ethan: Stochastic Gradient Descent with Adaptive Data: Applications to Online Learning
Che Ethan1 , Dong Jing1 , Tong Xin2
1 - Columbia Business School (United States)
2 - National University of Singapore (Singapore)

Abstract: We consider a stochastic optimization problem where the data is generated sequentially via
Markov chains whose dynamics are determined by the decision variables and the loss function is definedwith respect to the invariant measures of the Markov chains. Examples include finding the optimal priceto maximize the long-run average revenue minus waiting cost in queueing systems; finding the optimalorder-up-to level to minimize the long-run average holding cost plus backlog cost in inventory systems,
etc. We study the convergence of the√ corresponding stochastic gradient descent algorithm with adaptive
data. Our results establish an O(1/ T ) convergence rate when the loss function is convex and an O(1/T)
convergence rate when the loss function is strongly convex under reasonably general conditions on theergodicity of the Markov chains and smoothness of the loss function. We demonstrate how to apply
our results to several online-learning problems in operations research and a policy gradient algorithm inreinforcement learning.
Chen Xinyun: Data Pooling for Personalized Intervention via Contextual Bandits
Chen Xinyun1 , Shi Pengyi2 , Pu Shanwen3
1 - The Chinese University of Hong Kong, Shenzhen (China)
2 - Purdue University (United States)
3 - Shanghai University of Finance and Ecnomics (China)

Abstract: There is a growing interest in personalized preventative intervention across various fields,
including healthcare and community-based support programs. However, personalization poses uniquechallenges in learning and optimization due to limited data samples. To overcome this challenge, we
model the online optimal intervention problem as a contextual bandit with different target classes. To
improve the sample efficiency of personalized planning and address the issue of small sample, we develop
a novel data-pooling technique that is applicable to a broad class of contextual bandit algorithms. The
effectiveness of our approach is demonstrated through theoretical regret analysis, simulation experiments,and empirical studies involving real datasets obtained from a partner hospital and a public probation
dataset from the Department of Justice.
Kumar Rachitesh: Online Resource Allocation under Horizon Uncertainty
Balseiro Santiago1 , Kroer Christian1 , Kumar Rachitesh1
1 - Columbia University (United States)

Abstract: We study stochastic online resource allocation: a decision maker needs to allocate limited
resources to stochastically-generated sequentially-arriving requests in order to maximize reward. At eachtime step, requests are drawn independently from a distribution that is unknown to the decision maker.
Online resource allocation and its special cases have been studied extensively in the past, but prior results
crucially and universally rely on the strong assumption that the total number of requests (the horizon) is
known to the decision maker in advance. In many applications, such as revenue management and online
advertising, the number of requests can vary widely because of fluctuations in demand or user traffic
intensity. In this work, we develop online algorithms that are robust to horizon uncertainty. In sharp
contrast to the known-horizon setting, no algorithm can achieve even a constant asymptotic competitive
ratio that is independent of the horizon uncertainty. We introduce a novel generalization of dual mirror
descent which allows the decision maker to specify a schedule of time-varying target consumption rates,and prove corresponding performance guarantees. We go on to give a fast algorithm for computing
a schedule of target consumption rates that leads to near-optimal performance in the unknown-horizon
setting. In particular, our competitive ratio attains the optimal rate of growth (up to logarithmic factors)
as the horizon uncertainty grows large. Finally, we also provide a way to incorporate machine-learned

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predictions about the horizon which interpolates between the known and unknown horizon settings.

3.4.7

Invited session T1-7 (Olvera-Cravioto): Graph dynamics (Room 106)

Thursday 29th, 10:30 - 12:00 - chaired by Mariana Olvera-Cravioto
Bhamidi Shankar: Dynamic networks: one has to do the "math"Bhamidi Shankar1
1 - Department of Statistics and Operations Research, University of North Carolina (United States)

Abstract: Models for networks that evolve and change over time are ubiquitous in a host of domains
in applied probability, including modeling social networks, understanding the evolution of systems inproteomics, the study of the growth and spread of epidemics etc. This talk will give a brief summaryof three recent findings in this area: (a) Understanding the effect and detectability of change point in
the evolution of the system dynamics. (b) Reconstructing the initial "seed" that gave rise to the current
network, sometimes referred to as Network Archeology. (c) The disparity in the behavior of different
centrality measures for measuring popularity in settings where there are vertices of different types. The
main goal will to be convey unexpected findings in each of these three areas. Based largely on joint workwith Sayan Banerjee, Iain Carmichael, Nelson Antunes and Vladas Pipiras.
Contat Alice: Critical core percolation on random graphs
Contat Alice1
1 - Laboratoire de Mathématiques dÃŞrsay (France)

Abstract: Motivated by the desire to construct large independent sets in random graphs, Karp andSipser modified the usual greedy construction to yield an algorithm that outputs an independent set witha large cardinal called the Karp-Sipser core. When run on the Erdös-Rényi G(n, c/n) random graph,this algorithm is optimal as long as c < e. We will present the proof of a physics conjecture of Bauer
and Golinelli (2002) stating that at criticality, the size of the Karp-Sipser core is of order n3/5 . Alongthe way we shall highlight the similarities and differences with the usual greedy algorithm and the k-corealgorithm. Based on a joint work with Nicolas Curien and Thomas Budzinski.
Ray Rounak: Universality of the local limit of preferential attachment models
Ray Rounak1 , Van Der Hofstad Remco, Hazra Rajat Subhra, Garavaglia Alessandro
1 - Department of Mathematics and Computer Science, Eindhoven University of Technology (Netherlands)

Abstract: We study preferential attachment models where vertices enter the network with i.i.d. random
numbers of edges that we call the out-degree. We identify local limit of such models, substantiallyextending the work of Berger et al. [2014]. The degree distribution of this limiting random graph,
which we call the random Pólya point tree, has a surprising size-biasing phenomenon. Many of theexisting preferential attachment models can be viewed as special cases of our preferential attachment
model with i.i.d. out-degrees. Additionally, our models incorporates negative values of the preferential
attachment fitness parameter, which allows us to consider preferential attachment models with infinitevariance degrees. Our proof of local convergence consists of two main steps: a Pólya urn description of
our graphs, and an explicit identification of the neighbourhoods in them. We provide a novel and explicit
proof to establish a coupling between the preferential attachment model and the Pólya urn graph. Our
result proves a density convergence result, for fixed ages of vertices in the local limit.
Wang Tiandong: Random Networks with Heterogeneous Reciprocity
Wang Tiandong1
1 - Fudan University [Shanghai] (China)

Abstract: Users of social networks display diversified behavior and online habits. For instance, a user’s
tendency to reply to a post can depend on both the user and the person posting. For convenience, wegroup users into aggregated behavioral patterns, focusing here on the tendency to reply or reciprocate
to a message. The reciprocity feature in social networks reflects the information exchange among users.
We study properties of a preferential attachment model with heterogeneous reciprocity levels and givethe growth rate of model edge counts as well as prove convergence of empirical degree frequencies to alimiting distribution. This limiting distribution is not only multivariate regularly varying, but also has
the property of hidden regular variation.

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77

Invited session T1-8 (Dorsman, Righter & Gardner): Load balancing
II (Room 202)

Thursday 29th, 10:30 - 12:00 - chaired by Jan-Pieter Dorsman, Rhonda Righter & Kristen
Gardner
Mukherjee Debankur: Load Balancing with Data Locality: Extending Mean-Field Framework to Constrained Heterogeneous Systems
Mukherjee Debankur1 , Rutten Daan1 , Zhao Zhisheng1 , Wu Ruoyu2
1 - School of Industrial and Systems Engineering [Georgia Tech] (United States)
2 - Iowa State University (United States)

Abstract: Over the past few years, the load balancing problem on bipartite graphs has gained popularity
as it nicely models the data locality constraints in modern data centers. Due to such constraints, servers
violate the exchangeability assumption that has been pivotal for the classical mean-field analysis. We will
discuss several recent progress in this direction for broad classes of bipartite graphs including expanders,spatial graphs, and inhomogeneous graphs, each of which leads to a fundamentally distinct asymptotic
behavior.
Jhunjhunwala Prakirt: Join-the-Shortest Queue with Abandonment: Critically Loaded andHeavily Overloaded Regimes
Jhunjhunwala Prakirt1
1 - Georiga Institute of Technology (United States)

Abstract: We consider a Load Balancing system operating under the well-known Join-the-Shortest Queue(JSQ) policy, where jobs/customers are impatient, and abandon if they do not receive service after some(random) time. In this setting, we study the effect of these abandonments on the steady-state queue
length behavior. In particular, we characterize the ‘distribution of appropriately centered and scaled
steady-state queue length’ (hereafter referred to as limiting distribution) as the abandonment rate goesto zero. Depending on the arrival, service, and abandonment rates, we consider three different regimesof operation, each yielding a different steady-state distribution. The first regime is when the system
is underloaded, where we show that abandonments do not affect the limiting distribution. As a result,
JSQ with Abandonment (JSQ-A) asymptotically behaves like JSQ without abandonments, for which thelimiting distribution is known to be exponential. As the load increases, we move to the second regime,
where abandonments become significant, and there is a critical balance between the arrivals/services
and the abandonments. As such, the system undergoes a phase transition and the limiting distributionchanges from exponential to a truncated normal. As the load increases even further, the system enters
the heavily overloaded regime and the queue lengths become very large. In this case, we show thatthe (centered) steady state queue length distribution converges to a normal distribution. Our results
use the Transform method to establish a one-dimensional differential equation in terms of the MGF(Moment Generating Function) of the steady-state queue lengths, which can be solved to get the limiting
distribution.
Zubeldia Martin: Delayed Load Balancing
Zubeldia Martin1
1 - University of Minnesota [Twin Cities] (United States)

Abstract: We study a system comprimsed of a large number of homogeneous single-server queues, wherea central dispatched is entrusted with dispatching an incoming stream of jobs based on information
obtained through message exchanges with the servers. We assume that either these messages are delayed,or that the jobs are delayed when they are dispatched (that is, it takes a positive amount of time forjobs to arrive at a queue after they are dispatched). In this setting, we show that if the delays are smallenough, it is still possible to achieve asymptotically zero queueing delay (as the number of servers goesto infinity). Alternatively, if the delays are above certain threshold, the expected queueing delays must
be bounded away from zero.
Doroudi Sherwin: Queue-Length-Aware Dispatching in Large-Scale Heterogeneous Systems
Abdul Jaleel Jazeem1 , Doroudi Sherwin2 , Gardner Kristen3
1 - NXP Semiconductors [San Jose] (United States)
2 - University of Minnesota [Twin Cities] (United States)
3 - Amherst College (United States)

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Abstract: One dominant approach for reducing response times in large-scale systems is Join-the-ShortestQueue-d (JDQ-d): whenever a job arrives, the dispatcher queries d servers at random, and then assigns
the job to the queried server with the shortest queue. While JSQ-d is known to perform quite well insystems where all servers run at the same speed, this is not the case in systems that exhibit heterogeneitywith respect to server speeds. Unfortunately, there is no straightforward way to extend JSQ-d (or otherso-called "power-of-d" policies) to heterogeneous systems. Should a job be assigned to the queried serverwith the shortest queue even if much faster servers were among those queried? Should a job be assigned
to the queried server where it is expected to complete the soonest even if there is an idle, albeit slower,server available among those queried? And for that matter, should we query faster servers more often thantheir slower counterparts? Recent work has introduced a framework for developing strong dispatchingpolicies by pairing suitably chosen querying and assignment rules. Within this framework, prior work
has focused on finding strong-performing dispatching policies that use only the idle/busy statuses of thequeried servers, rather than detailed queue length information. In this paper, we overcome the challengeof evaluating the performance of-and finding strong-performing-general scalable dispatching policies thatmake use of both server speed and detailed queue length information, through a combination of meanfield analysis and a sequence of modified optimization problems. We find that well-designed lengthaware dispatching policies can significantly outperform their idleness-based counterparts in large-scale
heterogeneous systems. While the best policies of this kind are often complicated to describe, we findthat in the vast majority of cases the relatively simple Shortest Expected Wait policy performs nearly as
well, without the need for an especially sophisticated and time-consuming optimization procedure.

3.4.9

Invited session T1-10 (Torrisi): Topics on point processes models :theory and applications (Room 204)

Thursday 29th, 10:30 - 12:00 - chaired by Nicolas Privault
Flint Ian: An Equilibrium Spatial Model of Crime and Surveillance
Flint Ian1
1 - Institut Mines-Télécom [Paris] (France)

Abstract: The spatial heterogeneity of crime incidence arises from the interplay between criminals, who
decide where to commit crimes, and the police, who decide where to offer protection. In order to study thisinterdependence, we develop a game theoretical framework in which crime locations and police units aremodeled as point processes. We derive (i) the equilibrium distribution of crime and police, (ii) the average
probability of catching criminals, and (iii) the crime rates in terms of the economy fundamentals (given
by, among others, the spatial preferences of criminals, police expenditures, inequality, and poverty). The
theoretical results explain several empirical findings and our framework uncovers new processes driving
the spatial distribution of crime.
Kirchner Matthias: Critical cluster cascades
Kirchner Matthias1
1 - University of Teacher Education NMS Bern (Switzerland)

Abstract: We consider a sequence of Poisson cluster point processes on Rd : At step n ∈ N0 of theconstruction, the cluster centers have intensity c/(n + 1) for some c > 0, and each cluster consists ofthe particles of a branching random walk up to generation n—generated by a point process with mean
1. We show that this critical cluster cascade’ converges weakly, and that either the limit point processequals the a.s. void process (extinction), or it has the same intensity c as the critical cluster cascade(persistence). We obtain persistence, if and only if the Palm version of the outgrown critical branchingrandom walk is locally a.s. finite. This result allows us to give numerous examples for persistent criticalcluster cascades. In this talk, we first illustrate the construction and the main result. Then, we discuss
properties and possible applications of the limit point process in the persistent case.
Privault Nicolas: Closed-form computation of the Hawkes cumulants with exponential kernels
Privault Nicolas1
1 - Division of Materials Science, Nanyang Technological University (Singapore)

Abstract: We present an algorithm for the computation of multivariate cumulants of Hawkes processes,
based on a recursion for probability generating functionals and Bell polynomials. This approach yields

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exact analytical expressions of any orders and is easier to apply to higher-order cumulants in comparison
with other methods based on differential equations, tree enumeration or martingale arguments. Implementations in Maple, Mathematica and Sage are available, and provide a better alternative to Monte
Carlo estimates via stochastic simulations. The closed-form modeling of neuronal membrane potentials
driven by spike trains in a multivariate Hawkes process model with excitation and inhibition is considered as an application. N. Privault, Recursive computation of the Hawkes cumulants, Statistics &Probability Letters 177 (2021) Paper No. 109161. https://arxiv.org/abs/2012.07256 N. Privault, An
algorithm for the computation of joint Hawkes moments with exponential kernel, Proceedings of the
53rd ISCIE International Symposium on Stochastic Systems Theory and Its Applications (SSS’21), 2022.
https://doi.org/DOI:10.5687/sss.2022.72 N. Privault and M. Thieullen, Closed-form modeling of neuronal
spike train statistics by multivariate Hawkes processes, Physical Review E: Biological physics 106 (2022)
054410. https://arxiv.org/abs/2210.15549

3.5

Thursday 29th, 13:30 - 15:00

3.5.1

Invited session T2-1 (Ramanan): Measure-valued process limits ofstochastic networks (Auditorium)

Thursday 29th, 13:30 - 15:00 - chaired by Kavita Ramanan
Atar Rami: Many-server queues, invariant states, and entropy
Atar Rami1 , Kang Weining2 , Kaspi Haya1 , Ramanan Kavita3
1 - Technion (Israel)
2 - University of Maryland (United States)
3 - Brown University (United States)

Abstract: The fluid limit of the GI/G/N+G queue in the many-server (N → ∞) regime is described by anevolution equation in the space of measures, where a measure represents the age profile of the jobs being
processed. This equation can be classified as a transport equation with a nonlinear boundary condition.
Whereas it is easy to calculate an invariant state, it is more difficult to prove that it describes the only
possible long time behavior of this equation. Our result states that the only invariant distribution of theequation is the one given as an atom at the above “easy to guess” invariant state. One of the techniques
used to achieve that, which is based on relative entropy, will be described. The result complements aline of work by Kang and Ramanan, and in particular allows us to state that the N -limit-t-limit diagram
commutes. This is joint work with Weining Kang, Haya Kaspi, and Kavita Ramanan.
Maillard Pascal: Algorithmic hardness thresholds for the continuous random energy model
Maillard Pascal1
1 - Institut de Mathématiques de Toulouse (France)

Abstract: I will review recent results on algorithmic hardness thresholds for optimization and samplingin Derrida’s continuous random energy model (CREM). The CREM is a toy model for spin glasses andother random non-convex functions on high-dimensional spaces. Our results may shed light on theoreticalbarriers of optimization and sampling algorithms in general settings.
Véber Amandine: A multitype growth-fragmentation process to model the growth of afilamentous fungus
Véber Amandine1
1 - MAP5, CNRS, Université Paris Cité (France)

Abstract: Filamentous fungi form a large family of species playing an important role in the functioningof many ecosystems. They develop in space thanks to the growth and multiplication of filaments (alsocalled hyphae) which allow the absorption and sharing of nutrients and other molecules. In this talk, weshall present a toy model for the development of a hyphal network, whose main aim is to identify a small
number of key parameters describing the growth of the fungus in homogeneous conditions (in particular,
in lab conditions) and to understand and quantify the impact of different forms of stress on this growth.
The results presented are joint work with Vincent Bansaye (Ecole Polytechnique), Lena Kuwata (Univ.
Paris Cité) and Milica Tomasevic (CNRS and Ecole Polytechnique) on the maths side, and Cécilia Bobée,
Florence Chapeland-Leclerc, Thibault Chassereau, Pascal David, Eric Herbert, Christophe Lalanne, Clara
Ledoux, Gwenaël Ruprich-Robert, all at LIED (Univ. Paris Cité) on the biology and physics side.

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Yasodharan Sarath: Large deviations of interacting jump processes on locally tree-likegraphsRamanan Kavita1 , Yasodharan Sarath1
1 - Brown University (United States)

Abstract: We study countable-state semi-Markov locally interacting particle systems on sparse randomgraphs in which the state at a vertex evolves depending on its age and the states and ages of its neighboring
vertices. For certain sequences of random graphs, including Erdos-Renyi graphs G(n,c/n), we show thatas the number of vertices becomes large, the family of component empirical measures satisfies a large
deviation principle on the space of marked rooted graphs. The rate function is given in terms of certain
relative entropies. This talk is based on joint work with Kavita Ramanan.

3.5.2

Invited session T2-2 (Kanavetas): Strategic queueing V (Room 101)

Thursday 29th, 13:30 - 15:00 - chaired by Odysseos Kanavetas
Kanavetas Odysseas: The effect of reneging in a fluid queue with alternating service process
Kanavetas Odysseas1 , Logothetis Dimitrios1 , Manou Athanasia2
1 - Mathematical Institute, Leiden University (Netherlands)
2 - Department of Mathematics, National and Kapodistrian University of Athens (Greece)

Abstract: We consider the fluid version of a queueing system that alternates between slow and fast
service periods. We assume that customers (that are modeled as infinitesimal units at a fluid) decide
whether to join or balk upon arrival and they have the option of abandoning the system at any time,
without being served. In this system, we determine equilibrium strategies and evaluate the performance
under equilibrium by computing the throughput and the social welfare. We also explore the effect ofsystem parameters on these performance measures. Moreover, we explore the value of reneging option
by comparing the performance measures of this system to the corresponding measures of a system withthe reneging option closed.
Economou Antonis: The impact of informing strategic customers about other customers
decisions
Economou Antonis1
1 - National and Kapodistrian University of Athens (Greece)

Abstract: The impact of information on strategic customer behavior in queueing systems is a classical
topic that has attracted the interest of many researchers in the area of Rational Queueing. The information provided to the customers usually comprises the operation parameters of the system (as in theunobservable models), but it may also describe the state of the system, exactly or approximately (as in
the fully and partially observable models). In the present talk, we will present some ongoing work that focuses on the value of providing some information about the decisions of other customers. More concretely,
we will present a very simple stylized model where the customers are informed about other customers
decisions. We will analyze the strategic behavior in this case and determine the equilbrium strategies of
the customers. We will then compare the associated equlibrium throughput and social welfare of thismodel with the corresponding measures of the unobservable and observable models.
Baron Opher: Data Driven Causal Modeling of Queueing Systems
Baron Opher1 , Krass Dmitry1 , Senderovich Arik2 , Van Der Laan Mark3 , Xu Zhenghang1
1 - Rotman School of Management, University of Toronto (Canada)
2 - York University (Canada)
3 - University of California, Berkeley (United States)

Abstract: Simulation is a powerful tool for the prescriptive analysis of queueing models. With ample
data and expert knowledge of the underlying system structure, a good model can be constructed andused to predict impact of various interventions. However, such manual construction is both time- andskill-demanding. Moreover it is somewhat subjective - if the expert failed to note an important featureof the system (e.g. different customer types receiving different service priorities), the model will not be
accurate. As an alternative, we propose a data-driven representation of system building blocks, justified
by the G-computation results from causal inference literature. We describe the queueing data generation
process with structural equations and apply machine learning models to fit the equations to the data.

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Through numerical experiments, we show that this approach can replace the explicit queueing dynamics ofthe simulator. Our model is shown to capture the intervention effect in M/G/c queues with independent
hyper-exponential service time and first-come-first-serve queueing discipline. We discuss the limitationof this approach in the context of strategic customers.
Kerner Yoav: The Value of Service-Age Information in an Observable M/G/1 Queue
Kerner Yoav1 , Roet-Green Ricky2 , Zang Lin2
1 - Ben-Gurion University of the Negev (Israel)
2 - Simon School of business, University of Rochester (United States)

Abstract: We study the strategic join/balk behavior of customers in a fully observable M/G/1 queue.
Upon arrival, customers are informed about the queue length and about the service age. We assumed ahomogeneous reward from being served and waiting cost which is assumed to be linear. We obtain the
Nash equilibrium across customers. We focus on service time distributions with monotone mean residuallife and show that in such distributions the equilibrium strategy is threshold based, for any observed
queue length. We show that when the potential arrival rate is relatively high (low, respectively), thenproviding the service age information yields better (worse, respectively) system’s performance.

3.5.3

Invited session T2-3 (Buke): Many-Server Queues (Room 102)

Thursday 29th, 13:30 - 15:00 - chaired by Burak Buke
Bekker Rene: Multitasking Service Systems with Many Servers
Bekker Rene1 , Storm Jaap, Berkelmans Wouter
1 - Vrije Universiteit Amsterdam [Amsterdam] (Netherlands)

Abstract: We consider a many-server queue in which each server can serve multiple customers in parallel.
Such multitasking phenomena occur in various applications areas (e.g. in hospitals and contact centers),
although the impact of the number of customers that are simultaneously served on system efficiency may
vary. During the presentation, we aim to discuss diffusion limits of the queueing process under the QED
scaling and for different policies of assigning customers to servers depending on the number of customers
they serve. For a broad class of routing policies, including routing to the least busy server, the same
one-dimensional diffusion process is obtained in the heavy-traffic limit. In case of assignment to the mostbusy server, there is no state-space collapse and the diffusion limit involves a custom regulator mapping.
Moreover, the aim is to discuss some health applications where models with multi-tasking servers may
be useful.
Mukhopadhyay Arpan: Performance of Large Multi-Server Systems with Parallelisable Jobs
Mukhopadhyay Arpan1
1 - Department of Computer Science [Warwick] (United Kingdom)

Abstract: Modern data centres often handle computing jobs that are highly parallelisable. Such jobs
can be processed at multiple servers in parallel. Although parallel processing reduces the latency of jobs,
it comes at the cost of continuously maintaining synchronisation among parallel threads for each job.
Therefore, a natural question to ask is whether it is possible to achieve most of the benefits of parallelism
with only a small number of parallel threads per job. Motivated by this key question, in this talk, weshall consider a simple model in which each job can be served simultaneously by d distinct processor
sharing (PS) servers. The job is considered complete when the total amount of work done on it by all theservers equals its size. Under standard memoryless assumptions, we shall show that when the number n ofservers is large an exponential reduction in the average latency can be obtained using only d ≥ 2 parallel
servers per job compared to the case without parallelism (i.e., where d = 1). This is reminiscent of theclassical result of [Mitzenmacher, 1996] and [Dobrushin and Vvedenskaya, 1996] on the Power-of-d choices
load balancing algorithm and suggests that significant gains can be obtained with only small degree ofparallelism. The system is however significantly more challenging to analyse using as standard mean-fieldtechniques do not apply. We shall discuss how important properties such as stability, monotonicity, anduniform boundedness of the system can be established. We shall then discuss how these properties canbe combined to obtain asymptotic results in the large system limit.
This talk will be based on a joint work with A. Ganesh.

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Zhong Yueyang: Finite-Buffer Queues with Many Strategic Servers
Zhong Yueyang1 , Gopalakrishnan Raga2 , Ward Amy3
1 - The University of Chicago Booth School of Business (United States)
2 - Smith School of Business at Queen’s University (Canada)
3 - The University of Chicago Booth School of Business (United States)

Abstract: Traditional queueing theory assumes that servers work at constant speeds. That is reasonable
in computer science and manufacturing contexts. However, servers in service systems are people, and incontrast to machines, the incentives created by design decisions influence their work speeds. We study
how server work speed is affected by managerial decisions concerning (i) how many servers to staff andhow much to pay them and (ii) whether and when to turn away customers. We develop a game-theoretic
many-server Markovian queueing model with a finite or infinite buffer in which the work speeds emerge
as the solution to a non-cooperative game. In an asymptotic regime in which demand becomes large andthe utility function becomes concave, we establish existence, uniqueness, and monotonicity propertiesof underloaded, critically loaded, and overloaded equilibria for various regions in the design space. The
asymptotic regime we develop lays the foundation to study the interaction between customers that choose
whether or not to join the queue based on wait time, and servers that choose their work speeds. We
conclude by discussing some managerial consequences of ignoring strategic server behavior.
Buke Burak: Many-Server Systems with Heterogeneous and Strategic Servers
Buke Burak1
1 - The University of Edinburgh, School of Mathematics (James Clerk Maxwell Building The King’s Buildings Peter
Guthrie Tait Road Edinburgh EH9 3FD United Kingdom)

Abstract: In this talk, we focus on many server systems where servers are heterogeneous on an individuallevel. We present a framework to prove diffusion and fluid limits based on measure-valued processes that
address individual differences in servers. We also show how this framework can be generalized to include
parallel server systems with multiple pools of agents and customers. Finally, we show how this framework
can be applied to characterize asymmetric equilibrium when servers act strategically to experience more
idle times.

3.5.4

Invited session T2-4 (Nazarathy): Applied Probability for COVID-19(Room 103)

Thursday 29th, 13:30 - 15:00 - chaired by Peter Taylor
Asanjarani Azam: Emulation of epidemics via Bluetooth-based virtual safe virus spread
Asanjarani Azam1 , Shausan Aminath2 , Chew Keng2 , Graham Thomas2 , Henderson Shane3 , Jansen
Hermanus4 , Short Kirsty2 , Taylor Peter5 , Vuorinen Aapeli6 , Yadav Yuvraj7 , Ziedins Ilze1 , Nazarathy
Yoni2
1 - The University of Auckland (New Zealand)
2 - The University of Queensland (Australia)
3 - Cornell University [New York] (United States)
4 - Delft University of Technology (Netherlands)
5 - University of Melbourne (Australia)
6 - Columbia University (United States)
7 - Indian Institute of Technology Delhi (India)

Abstract: We describe the Safe Blues Android app experiment at the University of Auckland City
Campus. This experiment is designed to evaluate how physical interactions over time and between
individuals affect the spread of epidemics. Our experiment involves the voluntary use of the Safe Blues
Android app by participants. The app spreads multiple virtual safe virus strands via Bluetooth dependingon the social and physical proximity of the subjects. The evolution of the virtual epidemics is recorded asthey spread through the population. The data is presented as a real-time (and historical) dashboard. A
simulation model is applied to calibrate strand parameters. Participants’ locations are not recorded, butparticipants are rewarded based on the duration of participation within a geofenced area, and aggregate
participation numbers serve as part of the data.
Britton Tom: Disentangling the most effective behavioural changes to reduce transmission
during the Covid-19 pandemic
Britton Tom1
1 - Stockholm University (Sweden)

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Abstract: Using various data sources, including Google trend (behavioural changes) temperature and circulating virus strains, and reported number of cases and hospitalizations, we estimate how much transmission that can be attributed to various behavioural patterns (work, public transportation, super-markets
and shops/restaurants). The underlying infectious disease model also takes into account temperatureand virus strain effects. The analysis is performed on data from Sweden and Norway. (Joint work withF Gunther, H Kjelgaard Brustad and A Frigessi).
Juneja Sandeep: Agent based simulators for epidemic modelling: Simulating larger models
using smaller ones
Juneja Sandeep1 , Mittal Daksh, Agrawal Shubhada
1 - Tata Institute of Fundamental Research (India)

Abstract: Agent-based simulators (ABS) are a popular epidemiological modelling tool to study the
impact of various non-pharmaceutical interventions in managing an epidemic in a city (or a region). They
provide the flexibility to accurately model a heterogeneous population with time and location varying,
person-specific interactions as well as detailed governmental mobility restrictions. Typically, for accuracy,
each person is modelled separately. This however may make computational time prohibitive when the city
population and the simulated time is large. In this paper, we dig deeper into the underlying probabilistic
structure of a generic, locally detailed ABS for epidemiology to arrive at modifications that allow smaller
models (models with less number of agents) to give accurate statistics for larger ones, thus substantiallyspeeding up the simulation. We observe that simply considering a smaller aggregate model and scaling up
the output leads to inaccuracies. We exploit the observation that in the initial disease spread phase, thestarting infections create a family tree of infected individuals more-or-less independent of the other trees
and are modelled well as a multi-type super-critical branching process. Further, although this branchingprocess grows exponentially, the relative proportions amongst the population types stabilise quickly.
Once enough people have been infected, the future evolution of the epidemic is closely approximatedby its mean field limit with a random starting state. We build upon these insights to develop a shifted,
scaled and restart-based algorithm that accurately evaluates the ABS’s performance using a much smaller
model while carefully reducing the bias that may otherwise arise. We apply our algorithm for Covid-19
epidemic in a city and theoretically support the proposed algorithm through an asymptotic analysis where
the population size increases to infinity. We develop nuanced coupling based arguments to show that the
epidemic process is close to the branching process early on in the simulation.
Litvak Nelly: The role of inter-regional mobility in forecasting COVID-19 transimission in
the Netherlands
Litvak Nelly1,2 , Schoot Uiterkamp Martijn H. H.3 , Van Der Hofstad Remco2 , Gösgens Martijn2 ,Heesterbeek Hans4
1 - University of Twente [Netherlands] (Netherlands)
2 - Eindhoven University of Technology [Eindhoven] (Netherlands)
3 - Tilburg University [Tilburg] (Netherlands)
4 - Utrecht University [Utrecht] (Netherlands)

Abstract:
In The Netherlands, people comute from one municipality to another on daily basis, tothe office, to schools, and just for one meeting or a party. At the very beginning of the COVID-19
pandemic, we teamed up with a company who had the inter-regional mobility data, with the goal tohelp local governments in predicting and controling pandemic on a regional level. This grew into aninter-disciplinary project, with consortium of mathematicians, epedimeologists, public health experts,
policy scientists, and data science companies. In this talk I will present some of the results and lessons
learned from this project, First, I will discuss the trade-off between mobility and epidemic transmission.
I will present our heuristic to approximate optimal choices for mobility restrictions, using the mobility
data and clustering algorithms from Network Science. Next, I will discuss under which epidemiologicalcircumstances incorporating mobility into transmission models yields improvements in the accuracy of
forecasting. If time permits, I will also touch upon other aspects of the project: interdisciplinarity,
applications, and the organizational challenges around the use of the data.

3.5.5

Invited session T2-5 (Henry): Stochastic geometry (Room 104)

Thursday 29th, 13:30 - 15:00 - chaired by Benoît Henry
Blaszczyszyn Bartlomiej: Continuum Line-of-Sight Percolation on Poisson-Voronoi Tessellations
Blaszczyszyn Bartlomiej1

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1 - Inria de Paris (France)

Abstract: In this work, we study a new model for continuum line-of-sight percolation in a random
environment driven by the Poisson-Voronoi tessellation in the d-dimensional Euclidean space. The edges(one-dimensional facets, or simply 1-facets) of this tessellation are the support of a Cox point process,while the vertices (zero-dimensional facets or simply 0-facets) are the support of a Bernoulli point process.
Taking the superposition Z of these two processes, two points of Z are linked by an edge if and only
if they are sufficiently close and located on the same edge (1-facet) of the supporting tessellation. We
study the percolation of the random graph arising from this construction and prove that a 0-1 law, asubcritical phase as well as a supercritical phase exist under general assumptions. Our proofs are based ona coarse-graining argument with some notion of stabilization and asymptotic essential connectedness toinvestigate continuum percolation for Cox point processes. We also give numerical estimates of the criticalparameters of the model in the planar case, where our model is intended to represent telecommunications
networks in a random environment with obstructive conditions for signal propagation. This is a joint workwith Quentin Le Gall, Elie Cali, and Taoufik En-Najjary, published in Advances in Applied Probability
in 2021.
Coupier David: Percolation on Delaunay triangulation to model long-range connection in
5G networks
Coupier David1
1 - Institut Mines Télécom Nord Europe (France)

Abstract: The 5G’s Device-to-Device (D2D) technology consists in the possibility of short-range directcommunications (according to microscopic rules) between two devices or users without the need forthe signal to be routed through additional network infrastructure. Hence, a good connectivity in a
D2D network, i.e. a long-range connection (macroscopic property), can be naturally interpreted as apercolation problem. Our goal is to model D2D networks using stochastic geometry tools, in particular
Delaunay triangulation for the urban network and point processes for users, and then to study theirpercolation properties. Joint work with David Corlin Marchand and Benoît Henry (both from Institut
Mines Télécom).
Heydenreich Markus: Phase transition of geometric inhomogeneous percolation models
Heydenreich Markus1
1 - Augsburg University (Germany)

Abstract: We investigate a geometric random graph model whose vertices are given as a marked Poissonprocess on Rd . Edges are inserted between any pair of points independently with probability dependingon the Euclidean distance of the two endpoints and their marks. Upon variation of the Poisson density,a percolation phase transition occurs under mild conditions: for low density there are finite connectedcomponents only, while for large density there is an infinite component almost surely. Our interest is on
the transition between the low- and high-density phase, where the system is critical. We establish that if
dimension is high enough and the mark distribution satisfies certain conditions, then an infrared boundfor the critical connection function is valid. As a consequence, we obtain that various critical exponents
exist and take on their mean-field values. We finally present an asymptotic expansion of the criticaldensity as a function of the dimension.
Jahnel Benedikt: Subcritical percolation phases for generalized weight-dependent random
connection models
Jahnel Benedikt1
1 - Weierstrass Institute Berlin & TU Braunschweig (Germany)

Abstract: We present results on the existence of a subcritical percolation phase for a wide range of continuum percolation models where each vertex is embedded into Euclidean space and carries an independent
weight. In contrast to many established models, the presence of an edge is not only allowed to dependon the distance and weights of its end vertices but can also depend on the surrounding vertex set. Our
result can be applied in particular to models combining heavy-tailed degree distributions and long-range
effects, which are typically well connected. Moreover, we establish bounds on the tail-distribution of thenumber of points and the diameter of the subcritical component of a typical point.

3.5.6

Invited session T2-6 (Sanders): Stochastic learning in structured systems (Room 105)

Thursday 29th, 13:30 - 15:00 - chaired by Jaron Sanders

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Goldsztajn Diego: Utility maximizing load balancing policies
Goldsztajn Diego1 , Borst Sem1 , Van Leeuwaarden Johan2
1 - Eindhoven University of Technology [Eindhoven] (Netherlands)
2 - Tilburg University [Tilburg] (Netherlands)

Abstract: We consider a service system where incoming tasks are instantaneously assigned to one outof many heterogeneous server pools. All the tasks sharing a server pool are executed in parallel and theexecution times do not depend on the class of the server pool or the number of tasks currently contending
for service. However, associated with each server pool is a utility function that does depend on theclass of the server pool and the number of tasks currently sharing it. These features are characteristic of
streaming and online gaming services, where the duration of tasks is mainly determined by the application
but congestion can have a strong impact on the quality-of-service (e.g., video resolution and smoothness).
We provide an upper bound for the mean aggregate utility in steady state and we introduce a multithreshold policy that achieves the upper bound in a large-scale regime. Since the optimal threshold values
depend on the unknown offered load (i.e., the demand) of the system, our multi-threshold policy includes
a learning scheme that adjusts the thresholds over time in order to find the optimal values.
Lee Junghyun: Nearly Optimal Latent State Decoding in Block MDPs
Jedra Yassir1 , Lee Junghyun2 , Proutiere Alexandre1 , Yun Se-Young2
1 - KTH School of Electrical Engineering and Computer Science (Sweden)
2 - Korea Advanced Institute of Science and Technology (South Korea)

Abstract: We consider the problem of model estimation in episodic Block MDPs. In these MDPs, thedecision maker has access to rich observations or contexts generated from a small number of latent states.
We are interested in estimating the latent state decoding function (the mapping from the observations tolatent states) based on data generated under a fixed behavior policy. We derive an information-theoreticallower bound on the error rate for estimating this function and present an algorithm approaching thisfundamental limit. In turn, our algorithm also provides estimates of all the components of the MDP.
We apply our results to the problem of learning near-optimal policies in the reward-free setting. Basedon our efficient model estimation algorithm, we show that we can infer a policy converging (as thenumber of collected samples grows large) to the optimal policy at the best possible asymptotic rate. Our
analysis provides necessary and sufficient conditions under which exploiting the block structure yields
improvements in the sample complexity for identifying near-optimal policies. When these conditions aremet, the sample complexity in the minimax reward-free setting is improved by a multiplicative factor n,where n is the number of contexts.
Senen-Cerda Albert: Asymptotic convergence rate of Dropout in neural network models
Senen-Cerda Albert1 , Sanders Jaron1
1 - Eindhoven University of Technology [Eindhoven] (Netherlands)

Abstract: Dropout (Hinton et al., 2012) is a well-known method to prevent overfitting in neural networks that temporarily ’drops out’ nodes of the network at random during each step of the training withstochastic gradient descent. In this talk we present general convergence guarantees for training neuralnetworks with dropout or one of its variants, and investigate its convergence rate using stochastic approximation techniques on simplified models for deep and shallow neural networks. In particular, we examine
theoretically and with simulations the dependence of the convergence rate on the width and depth of
the neural network, and more importantly, on the dropout probability. For the considered models, we
obtain explicit bounds for the convergence rate that depend on the dropout probability and discuss theirconsequences. This talk is based on results from two articles and is joint work with Jaron Sanders.
Van Werde Alexander: Detection and evaluation of clusters within sequential data
Van Werde Alexander1
1 - Eindhoven University of Technology (Netherlands)

Abstract: Motivated by theoretical advancements in dimensionality reduction techniques we use a recent
model, called Block Markov Chains, to conduct a practical study of clustering in real-world sequential
data. Clustering algorithms for Block Markov Chains possess theoretical optimality guarantees and canbe deployed in sparse data regimes. Despite these favorable theoretical properties, a thorough evaluationof these algorithms in realistic settings has been lacking.

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We address this issue and investigate the suitability of these clustering algorithms in exploratory data
analysis of real-world sequential data. In particular, our sequential data is derived from human DNA,
written text, animal movement data and financial markets. In order to evaluate the determined clusters,
and the associated Block Markov Chain model, we further develop a set of evaluation tools. These
tools include benchmarking, spectral noise analysis and statistical model selection tools. An efficient
implementation of the clustering algorithm and the new evaluation tools is made available together withthis paper.
Practical challenges associated to real-world data are encountered and discussed. It is ultimately
found that the Block Markov Chain model assumption, together with the tools developed here, canindeed produce meaningful insights in exploratory data analyses despite the complexity and sparsity ofreal-world data. This talk is based on arXiv:2210.01679.

3.5.7

Invited session T2-7 (Avrachenkov): Network inference I (Room 106)

Thursday 29th, 13:30 - 15:00 - chaired by Kostia Avrachenkov
Gösgens Martijn: The Hyperspherical Geometry of Community Detection
Gösgens Martijn1 , Van Der Hofstad Remco1 , Litvak Nelly1,2
1 - Eindhoven University of Technology [Eindhoven] (Netherlands)
2 - University of Twente (Netherlands)

Abstract: We introduce a metric space of clusterings, where clusterings are described by a binary vector
indexed by the vertex-pairs. We extend this geometry to a hypersphere and prove that maximizing
modularity is equivalent to minimizing the angular distance to some modularity vector over the set ofclustering vectors. In that sense, modularity-based community detection methods can be viewed as a
subclass of a more general class of projection methods, which we define as the community detection
methods that adhere to the following two-step procedure: first, mapping the network to a point on thehypersphere; second, projecting this point to the set of clustering vectors. We show that this class ofprojection methods contains many interesting community detection methods. Many of these new methods
cannot be described in terms of null models and resolution parameters, as is customary for modularitybased methods. Instead, they are more naturally characterized by the latitude and the meridian of thehypersphere point that the network is mapped to. We discuss how this latitude and meridian affect theclustering that results from applying the projection method.
Leskelä Lasse: Community recovery from non-binary and higher-order network interactions
Leskelä Lasse1
1 - Aalto University (Finland)

Abstract: Community recovery is the task of learning a latent community structure from interactions ina population of N nodes. Efficient algorithms for sparse binary pairwise interaction data are well known,and so are their consistency properties with respect to data sampled from the stochastic block model(SBM), the canonical model for random graphs with a community structure. Instead of a binary variableindicating whether or not an interaction occurs, we often also observe a category, value, or shape of aninteraction. This motivates the definition of a generalised SBM in which interactions can be of arbitrary
type, including categorical, numeric, and vector-valued, and not excluding even more general objects
such as Markov chains or Poisson processes. For this model, I will discuss information-theoretic bounds
which characterise the existence of consistent estimators in terms of data sparsity, statistical similarity
between intra- and inter-block interaction distributions, and the shape and size of the interaction space.
Temporal networks with time-correlated interaction patterns of length T provide an important model
instance, for which consistency can be analysed with respect to either N or T, or both, approaching
infinity. Time permitting, I will also highlight recent findings and open problems related to data sets
involving higher-order interactions which can be modelled using hypergraph stochastic block models.
Matias Catherine: Model-based clustering in simple hypergraphs through a stochastic
blockmodel
Matias Catherine1
1 - Laboratoire de Probabilités, Statistique et Modélisation (France)

Abstract:
We present a new hypergraph stochastic blockmodel and an associated inference procedure for model-based clustering of the nodes in simple hypergraphs. Simple hypergraphs, where a node
may not appear several times in a same hyperedge, have been overlooked in the literature, though

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they appropriately model some high-order interactions (such as co-authorship). The model assumes
latent groups for the nodes and conditional independence of the hyperedges given the latent groups.
We establish the first proof of generic identifiability of the parameters in such a model. We develop avariational approximation Expectation-Maximization algorithm for parameter inference and node clustering, and derive an integrated classification likelihood criterion for model selection. We illustrate
the performance of our algorithm on synthetic data and analyse a real dataset of co-authorship. Our
method called HyperSBM is implemented in C++ for efficiency and available as an R package athttps://github.com/LB1304/HyperSBM.
Xu Jiaming: Random Graph Matching in Geometric Models
Xu Jiaming1
1 - Duke university [Durham] (United States)

Abstract: This talk focuses on the problem of matching two complete graphs with edge weights correlated
through latent geometries, extending a recent line of research on random graph matching with independent edge weights to geometric models. We derive an approximate maximum likelihood estimator andcharacterize the sufficient conditions under which it achieves perfect recovery or almost perfect recovery.
These conditions are further shown to be information-theoretically optimal even when the latent coordinates are directly observed. This work bridges several streams of literature including planted matching,feature matching, Procrustes matching, and graph matching. Based on joint work with Haoyu Wang,
Yihong Wu, and Israel Yolou from Yale. Preprint available at https://arxiv.org/pdf/2202.10662.pdf

3.5.8

Invited session T2-8 (Dorsman, Righter & Gardner): Load balancing
III (Room 202)

Thursday 29th, 13:30 - 15:00 - chaired by Jan-Pieter Dorsman, Rhonda Righter & Kristen
Gardner
Wang Weina: The M/M/k with Deterministic Setup Times
Williams Jalani1 , Harchol-Balter Mor1 , Wang Weina1
1 - Computer Science Department - Carnegie Mellon University (United States)

Abstract: Capacity management, whether it involves servers in a data center, or human staff in a callcenter, or doctors in a hospital, is largely about balancing a resource-delay tradeoff. On the one hand,
one would like to turn off servers when not in use (or send home staff that are idle) to save on resources.
On the other hand, one wants to avoid the considerable setup time required to turn an “off” server back“on.” However, surprisingly little is known about the effect of setup times on delay. While there has beensome work on studying the M/M/k with Exponentially-distributed setup times, these works provide only
iterative methods for computing mean delay, giving little insight as to how delay is affected by k, by load,
and by the setup time. Furthermore, setup time in practice is much better modeled by a deterministic
random variable. In this talk, we present the first analysis of the M/M/k with Deterministic setup times.
We provide bounds on the effect of setup on delay, where our bounds are highly accurate for the common
case where the setup time is much higher than the job service time. Our bounds are relatively simple
algebraic formulas which provide insights into how delay scales with the input parameters. Our proofs
use a combination of renewal theory, martingale arguments, and stopping-time constructions, providing
strong intuition on the transient behavior of a system that turns servers on and off.
Walton Neil: Stability and Optimization of Speculative Queueing Networks
Walton Neil1
1 - Neil Walton, Durham (United Kingdom)

Abstract: We provide a queueing-theoretic framework for job replication schemes based on the principle"replicate a job as soon as the system detects it as a straggler". This is called job speculation. Recent
works have analyzed replication on arrival, which we refer to as replication. Replication is motivated by its
implementation in Google’s BigTable. However, systems such as Apache Spark and Hadoop MapReduce
implement speculative job execution. The performance and optimization of speculative job execution is
not well understood. To this end, we propose a queueing network model for load balancing where eachserver can speculate on the execution time of a job. Specifically, each job is initially assigned to a single
server by a frontend dispatcher. Then, when its execution begins, the server sets a timeout. If the job
completes before the timeout, it leaves the network, otherwise the job is terminated and relaunched or

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resumed at another server where it will complete. We provide a necessary and sufficient condition for thestability of speculative queueing networks with heterogeneous servers, general job sizes and schedulingdisciplines. We find that speculation can increase the stability region of the network when comparedwith standard load balancing models and replication schemes. We provide general conditions underwhich timeouts increase the size of the stability region and derive a formula for the optimal speculation
time, i.e., the timeout that minimizes the load induced through speculation. We compare speculation
with redundant-d and redundant-to-idle-queue-d rules under an S&X model. For light loaded systems,
redundancy schemes provide better response times. However, for moderate to heavy loadings, redundancy
schemes can lose capacity and have markedly worse response times when compared with a speculative
scheme. (Joint work with Jonathan Anselmi)
Perry Ohad: Stability of parallel server systems
Moyal Pascal1 , Perry Ohad2
1 - Institut Élie Cartan de Lorraine (France)
2 - SMU (United States)

Abstract:
The fundamental problem in the study of parallel-server systems is that of finding andanalyzing routing policies of arriving jobs to the servers that efficiently balance the load on the servers.
The most natural, and well-studied policies are ’Join the shortest workload’ (JSW), ’Join the shortest
queue’ (JSQ), and the ’Power-of-d’ routing (PW(d)).
In this paper we study the stability problem of parallel-server systems, assuming that routing errors
may occur, so that arrivals may be routed to the “wrong” queue (not the smallest among the relevant
queues) with a positive probability. We treat this routing mechanism as a probabilistic routing policy,
named a p-allocation policy, where p is an s-dimensional vector whose components are the routing probabilities. We study the (in)stability problem of the system under this routing mechanism, and under its“non-idling” version. We first show that the stability region is precisely the usual “rho less than one”,
whenever p is dominated by the uniform probability for a suitable ordering of measures. Otherwise, weshow that the stability region is in general strictly smaller than the set “rho less than one”. Our analyses
build on Lyapunov techniques on an ordered space of s-dimensional real-valued vectors, and employing ageneralized form of the Schur-convex order.
Hyytiä Esa: Are highly scalable sequential dispatching policies asymptotically optimal?
Hyytiä Esa1 , Righter Rhonda
1 - University of Iceland (Department of Computer Science, University of Iceland, Dunhagi 5, 107 Reykjavik, Iceland;
Iceland)

Abstract: Job dispatching appears in many communication and computer systems. In this talk, we
present a classification of dispatching policies according to their scalability, and introduce the class ofsequential dispatching policies, which are a special case of index policies. Sequential policies are highly
scalable. We focus on a sequential policy where each server has a single control parameter that canbe determined in adaptive manner. In principle, the optimal (size-aware) policies can be determined
numerically via value iteration. This approach, however, becomes computationally demanding as the sizeof the system increases. In the numerical examples, we compare sequential policies to the optimal policy
and other well-known heuristic dispatching policies. Our numerical examples suggest that efficient andscalable load balancing can be realized by simple primitive dispatching elements. This approach seems
also suitable for machine learning.

3.5.9

Invited session T2-10 (Decreusefond): Stein method I (Room 204)

Thursday 29th, 13:30 - 15:00 - chaired by Laurent Decreusefond
Coutin Laure: Diffusive limits of Lipschitz functionals of Poisson measures
Coutin Laure1
1 - Mathematical Institute of Toulouse (France)

Abstract: Continuous Time Markov Chains, Hawkes processes and many other interesting processes canbe described as solution of stochastic differential equations driven by Poisson measures. First, using the
Stein’s method, we give the convergence rate of a sequence of renormalized Poisson measures towards
the Brownian motion in several distances, constructed on the model of the Kantorovitch-Rubinstein (orWasserstein-1) distance. We show that many operations (like time change, convolution) on continuousfunctions are Lipschitz continuous to extend these quantified convergences to diffusive limits of Markov
processes and long-time behavior of Hawkes processes.

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Nourdin Ivan: Total variation bound for Hadwiger’s functional using Stein’s method
Nourdin Ivan1
1 - University of Luxembourg [Luxembourg] (Luxembourg)

Abstract: Let K be a convex body in Rd . Let X be a d-dimensional random vector distributed according
to the Hadwiger-Wills density associated with K. Finally, let the information content H be definedas H = dist2 (X, K). In this talk, we will study the fluctuations of H around its expectation as thedimension d go to infinity. Stein’s method plays a crucial role in our analysis. This is joint work with
Valentin Garino.
Reveillac Anthony: The Malliavin-Stein method for Hawkes functionals
Reveillac Anthony1,2
1 - Institut de Mathématiques de Toulouse UMR5219 (France)
2 - Institut National des Sciences Appliquées - Toulouse (France)

Abstract: In this talk we combine the Malliavin calculus and Stein’s method to provide general bounds
on the Wasserstein distance between functionals of a compound Hawkes process and a given Gaussian
density. To achieve this, we rely on the Poisson embedding representation of an Hawkes process to provide
a Malliavin calculus for the Hawkes processes, and more generally for compound Hawkes processes. Asan application, we close a gap in the literature by providing the first Berry-Esséen bounds associatedto Central Limit Theorems for the compound Hawkes process. This talk is based on joint works with
Caroline Hillairet, Lorick Huang, Mahmoud Khabou and Nicolas Privault.
Vuong Christophe: Malliavin-Stein method for conditionally independent random variables
Vuong Christophe1 , Decreusefond Laurent1
1 - Télécom Paris (France)

Abstract: On any denumerable product of probability spaces, we extend the discrete Malliavin structure
for conditionally independent random variables. As a consequence, we obtain the chaos decomposition
for functionals of conditionally independent random variables. The Malliavin-Stein method yields BerryEsseen bounds for U-Statistics of such random variables. It leads to quantitative statements of conditional
limit theorems: Lyapunov’s central limit theorem, partial fourth moment theorem for multilinear forms.
As an application, we obtain rates of normal approximation for subhypergraph counts in random exchangeable hypergraphs.

3.6

Thursday 29th, 15:30 - 17:00

3.6.1

Invited session T3-1 (Blanchet): Advances in Theory and Algorithms
for Decision Making Under Uncertainty (Auditorium)

Thursday 29th, 15:30 - 17:00 - chaired by Jose Blanchet
Dong Jing: A primal-dual approach to constrained Markov decision processes with applications to queue scheduling and inventory management
Dong Jing1 , Chen Yi2 , Wang Zhaoran3
1 - Columbia University (United States)
2 - Hong Kong University of Science and Technology (Hong Kong SAR China)
3 - Northwestern University (United States)

Abstract: In many sequential decision-making problems, a single objective might not suffice to describe
the real considerations faced by decision-makers. The constrained Markov decision process (CMDP) has
become an important modeling tool in this context. In this talk, I will introduce a data-driven primal-dual
algorithm to solve CMDPs. Our approach alternatively applies regularized policy iteration to improve
the policy and subgradient ascent to maintain√ the constraints. Under mild regularity conditions, we showthat the algorithm converges at rate O(1/ T ). Our algorithm can be easily combined with advanced
value function approximation techniques to deal with large-scale problems, with the added benefit ofstraightforward convergence analysis. We apply the algorithm to solve multi-class queue schedulingand multi-product inventory control problems, and show that it generates policies that achieve superior
performance compared to state-of-art policies.

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Qu Yanlin: Contractive Drift Conditions for Rates of Convergence of Markov Chains in
Wasserstein Distance
Qu Yanlin1 , Blanchet Jose1 , Glynn Peter1
1 - Stanford University (United States)

Abstract: We introduce a condition called "contractive drift" to bound the convergence of Markov
chains in Wasserstein distance. Under this condition, we derive computable convergence bounds withdifferent rates, from polynomial to exponential. These bounds are computable in the sense that they only
contain fully explicit constants and one-step transition expectations. To enhance practical application,
we develop various techniques to verify the contractive drift condition so that computable convergencebounds can be conveniently obtained for queueing models as well as optimization algorithms. Moreover,
our bounds have the ability to capture optimal rates of convergence in terms of model parameters suchas the traffic intensity in queueing models and the step size in optimization algorithms.
Ghosh Soumyadip: Distributionally Robust Optimization of Boosting for Efficient Ensemble
Learning
Ghosh Soumyadip1 , Squillante Mark1
1 - IBM [Yorktown] (United States)

Abstract: Boosting is a learning approach that combines weak learners into an ensemble, where eachlearner is picked adaptively by training on data that are re-weighed to emphasize those data points on
which the current ensemble performs poorly. The ensemble’s output is obtained as a weighed combinationof the individual learner’s outputs. Various algorithms have been proposed to determine the data andweak learner weights, with the popular AdaBoost multiplicative weights scheme being the most prominent and having rigorously established theoretical properties. We propose and analyze a sequence ofdistributionally robust formulations for both re-weighing problems with the objective of picking a compact ensemble that optimizes generalization performance. We devise computationally efficient estimation
algorithms to implement these formulations, which provably balance a fundamental tradeoff between
computation time and statistical variance. Numerical experiments demonstrate the significant benefitsof our approach over previous related work.
Huang Zhiyuan: Conformal Data-Driven Chance Constraints
Lam Henry1 , Huang Zhiyuan1
1 - Columbia University (United States)

Abstract: Chance-constrained optimization is a widely popular approach to obtain decisions that balance
objective performance and risk, by requiring the solution to satisfy safety constraints with a high probability. In data-driven contexts, existing approaches are designed to attain such a high-probability feasibility
at a confidence level with respect to past data. This confidence guarantee, while intuitive, requires extra
parameter specification that adds sensitivity and conservativeness. We propose an alternative statisticalframework for data-driven chance-constrained optimization that gives conformal guarantees on the obtained decision. Our framework naturally integrates the probability level in the chance constraint andthe data confidence level, thus removing the confidence parameter, and subsequently leading to less conservative sample size requirements and better objective performances than existing methods. Moreover,
our framework applies to “conformalize” a range of more advanced methods including sampling-anddiscarding, FAST and learning-based robust optimization.

3.6.2

Invited session T3-2 (Koole): Call centers (Room 101)

Thursday 29th, 15:30 - 17:00 - chaired by Ger Koole
Garlati Elena: Fractional staffing levels in Erlang models
Garlati Elena1,2
1 - Vrije Universiteit, Amsterdam (Netherlands)
2 - Internship at CCmath BV (Netherlands)

Abstract: Even though Erlang calculators give powerful results on safety staffing decicions, the Markov
chains theory behind allows us to only work with integer staffing levels. In practice, when schedulingagents, an additional percentage is added to this staffing level. Since we are scheduling an integer numberof agents, we are effectively rounding twice. In order to avoid this we should get a fractional staffing

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level from the Erlang calculation. With this research, we give an overview on which are the different
approaches to extend the Erlang models to a continuous setting in terms of staffing levels with the purposeof making thousands of computations in fractions of seconds. First, we derive the re-formulation of theperformance measures as continuous functions in terms of number of agents for Erlang C systems from atheoretical point of view. Since this is not applicable to all the Erlang models, we subsequently introduce
new approaches for the research of fractional staffing levels in Erlang C in order to extend this problems
to other more complicated systems, such as Erlang A.
Li Siqiao: Data-driven prediction of service level for call centers
Li Siqiao1 , Akidas Hatim, Koole Ger
1 - Department of Mathematics, Vrije Universiteit Amsterdam (Netherlands)

Abstract: Predicting service levels accurately can help reduce over- /understaffing in workforce planningin call centers so as to save cost. The commonly used Erlang models in practice cannot capture agent
behaviors such as different average handling times, taking break patterns, and so on. Moreover, theErlang models fail in multi-skill situations. Whereas using simulation can capture more details in the callcenter, building a simulation model is challenging and expensive. In this work, we propose a data-driven
method: using one-year call center data to train a machine-learning model to predict the service level.
Our numerical results show that the proposed machine learning method is able to have a more accurate
prediction of the service level than Erlang C and Erlang A with negligible computing time. Our method
also requires less effort than Erlang models and simulation because dumping a historical dataset into themethod is all it needs.
Özümerzifon Ömer: Analysis of time-dependent queues with retrials: Impact of retrial
distributionÖzümerzifon Ömer1 , Legros Benjamin2 , Stolletz Raik1
1 - University of Mannheim (Germany)
2 - École de Management de Normandie (France)

Abstract:
Queueing systems are used in various service systems, such as call centers, health care,emergency services, and repair facilities. In many of these service systems, especially in call centers,
customers leave the queue before being served due to a lack of patience. However, impatient users may
join the system at a later time again after some time (retrials). This research project focuses on thetime-dependent performance evaluation of multi-server queueing systems with retrials, and the impactof retrial time distribution. In order to approximate performance measures in a time-dependent setting,
we develop a stationary backlog-carryover (SBC) approach. We present the basic ideas of this approachand findings of a numerical study.
Ger Koole: The validation of call center workforce staffing models
Ger Koole1
1 - Vrije Universiteit, Amsterdam (Netherlands)

Abstract: Forecasting and mathematical models are used on a large scale to predict call center performance. Validating these models is difficult because we have non-identical dependent realizations, defying
standard statistical validation methods. We discuss ways to decompose the errors into its components
such as forecasting error, noise and model error. This is ongoing work currently undertaken with Siqiao
Li.

3.6.3

Invited session T3-3 (Ayhan): Control of Queueing Systems (Room102)

Thursday 29th, 15:30 - 17:00 - chaired by Hayriye Ayhan
Lewis Mark: A stochastic decision-making scenario in Telemedicine
Lewis Mark1,2 , Pender Jamol2 , Lu Shuwen1
1 - Cornell University (United States)
2 - School of Operations Research and Information Engineering (United States)

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Abstract: We consider the question of when a nurse practitioner should call for a telemedicine consultation when low acuity patients are receiving service. A "minute clinic" is modeled using a stochastic
network. Where in the past nurse practitioners made these decisions without explcitly considering upstream effects, incorporating the impact on other patients complicates decision-making. After modelingthe clinic with a clearing system, we show when the structure of an optimal control is monotone and thendevelop heuristic strategies for implementation. Our results also provide guidance as to when a minuteclinic should NOT be equpped with a telemedicine capability.
Rebuffi Louis-Sébastien: Reinforcement Learning in a Birth and Death Process: Breaking
the Dependence on the State Space
Rebuffi Louis-Sébastien1,2 , Anselmi Jonatha2 , Gaujal Bruno2
1 - Université Grenoble Alpes (France)
2 - Inria (France)

Abstract: In this talk, we revisit the regret of undiscounted reinforcement learning in MDPs with abirth and death structure. Specifically, we consider a controlled queue with impatient jobs and the main
objective is to optimize a trade-off between energy consumption and user-perceived performance. Within
this setting, the diameter D of the MDP is Ω(S S ), where S is the √number of states. Therefore, theexisting lower and upper bounds on the regret at time T , of order O( DSAT ) for MDPs with S statesand A actions, may suggest that reinforcement learning is inefficient here. In our main result however,
we exploit the structure of our MDPs to show that the regret√ of a slightly-tweaked version of the classicallearning algorithm UCRL2 is in fact upper bounded by Õ( E2 AT ) where E2 is related to the weighted
second moment of the stationary measure of a reference policy. Importantly, E2 is bounded independentlyof S. Thus, our bound is asymptotically independent of the number of states and of the diameter. This
result is based on a careful study of the number of visits performed by the learning algorithm to thestates of the MDP, which is highly non-uniform.
Righter Rhonda: Policy-Space Collapse in Matching Models
Righter Rhonda1 , Xie Runhan, Gardner Kristen
1 - IEOR, UC Berkeley (IEOR UC Berkeley, CA 94720 United States)

Abstract: We consider one- and two-sided multi-class matching models and give sufficient conditions forpolicy-space collapse: the distribution of the number of each items of each class at any time is the same forany queue-position-based matching order, such as first-come-first-matched and last-come-first-matched.
An important special case is redundancy(d) models, both under cancel-on-complete and cancel-on-start
protocols.
Ayhan Hayriye: Optimal Pricing and Information Sharing in Queueing Systems
Ayhan Hayriye1 , Andradottir Sigrun1 , Xinchang Wang2
1 - School of Industrial and Systems Engineering [Georgia Tech] (United States)
2 - Department of Finance and Management Science, Carson College of Business, Washington State University (UnitedStates)Abstract: We study optimal pricing in a single-server queueing system that can be observable or unobservable, depending on how customers receive information to estimate sojourn time. Our primaryobjective is to determine whether the service provider is better off making the system observable orunobservable under optimal pricing. We formulate the optimal pricing problem using Markov decision
process (MDP) models for both observable and unobservable systems. For unobservable systems, theproblem is studied using an MDP with a fixed-point equation as equilibrium constraints. We show that
the MDPs for both observable and unobservable queues are special cases of a generalized arrivals-based
MDP model, in which the optimal arrival rate (rather than price) is set in each state. Then, we showthat the optimal policy that solves the generalized MDP exhibits a monotone structure in that the optimal arrival rate is non-decreasing in the queue length, which allows for developing efficient algorithms
to determine optimal pricing policies. Next, we show that if no customers overestimate sojourn time inthe observable system, it is in the interest of the service provider to make the system observable. We
also show that if all customers overestimate sojourn time, the service provider is better off making thesystem unobservable. Lastly, we learn from numerical results that when customers are heterogeneous inestimating their sojourn time, the service provider is expected to receive a higher gain by making thesystem observable if on average customers do not significantly overestimate sojourn time.

3.6.4

Invited session T3-4 (Tran): Contact-tracing (Room 103)

Thursday 29th, 15:30 - 17:00 - chaired by Viet Chi Tran

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Vo Thi Phuong Thuy: Exploration of SBM graphs by the Respondent Driven Sampling
methods.
Vo Thuy1
1 - Laboratoire de Mathématiques et Modélisation d’Evry (France)

Abstract: The study of Respondent Driven Sampling (RDS) is invested for the discovery of a social
network of hidden populations. It leads to the study of a Markov chain on a random graph whose vertices
represent individuals and whose edges describe the relationships between the people connected. The RDS
survey searches for hidden nodes in the population by randomly following the edges of the underlying
social network, which allows us to trace the sampled individuals. We are interested in the problem ofrecovering statistical information on a SBM from the subgraph discovered by an exploring random walk
(RDS with 1 coupon per interviewee). We consider here the dense case where the random network can
be approximated by a graphon. First, we write the probability of the subgraph discovered by the random
walk: biases emerge because the hubs and the majority types are more likely to be sampled. Even forthe case where the types are observed, the maximum likelihood estimator is not explicit any more. Whenthe types of the vertices are unobserved, we use an SAEM (Stochastic approximation of ExpectationMaximization) algorithm to maximize the likelihood. Second, we propose a different estimation strategy
using new results by Athreya and Röllin. It consists in de-biasing the variational EM estimator proposedin Daudin et al. and that ignores the biases.
Zhang Dongni: Epidemic models with manual and digital contact tracing
Zhang Dongni1 , Britton Tom1
1 - Stockholm University (Sweden)

Abstract: We start with a Markovian SIR epidemic model in a homogeneous mixing community with aconstant diagnosis rate. First, we introduce manual contact tracing by assuming that once an infective
is diagnosed (tested positive and isolated), each of her/his contacts is immediately traced and tested
independently with some fixed probability. Using large population approximations, we analyzed the earlystage of the outbreak when the process of "to-be-traced components" behaves like a branching process.
The component and individual reproduction numbers are derived. Then we focus on the more recent
digital contact tracing via a tracing app (only app-users can trigger and be traced by digital tracing).
And we assume that there is a fixed app-using fraction and that digital tracing occurs instantaneously
and recursively. The model with digital tracing is analysed by a two-type branching process relying ona large community, where one type is "app-using components," and another is non-app-users. Further,
we investigate the combined preventive effect of manual and digital tracing. This combined model isanalysed by a different two-type branching process with both types being the "to-be-traced components"
but starting with different "roots". The corresponding reproduction numbers are derived. We conclude
that to control the epidemic, we always need to have a large fraction of app-users compared to the fractionof individuals being successfully reached by manual tracing. Another important conclusion is that the
combined effect is bigger than the product of two separate preventive effects. Finally, our ongoing work
is based on an SEIR epidemic model on a configuration model, where transmission could happen fromrandom contacts (e.g., on a bus), which are usually more easily identified by using the tracing app.
Each infective remains infectious for a constant period. Meanwhile, we assume that such an infective isdiagnosed and asked for an interview with some probability otherwise, we say that the infective recovers
naturally. Once diagnosed, each of her/his infectee neighbours (manual tracing only on the network) is
reported with some probability independently. If such reported neighbours are infectious after some delay,
they are isolated and said to be traced. If the diagnosed person is also an app-user, all of her/his appusing contacts are traced immediately (digital tracing both on the network and among global contacts).
And in this case, we assume that only the diagnosed individuals can trigger contact tracing.
Stegehuis Clara: Contact tracing and network structures
Stegehuis Clara1
1 - University of Twente [Netherlands] (Netherlands)

Abstract:
Quarantining and contact tracing are popular practices to mitigate epidemic outbreaks.
However, the effectiveness of these practices depends on the network on which the epidemic spreads. We
therefore study how the final size of an epidemic is influenced by contact tracing and quarantining on a
network null model: the configuration model. In this process, infected vertices may self-quarantine andtrace their infector with a given success probability. A traced infector is less likely to infect others. We
show that the effectiveness of such tracing processes strongly depends on the network structure. However,in contrast to previous findings, the tracing procedure is not necessarily more effective on networks with

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heterogeneous degrees. We also show that network clustering influences the effectiveness of the tracing
process in a non-trivial way.
Doroudi Sherwin: Evaluating Disease Transmission Risks in Small-Scale Settings Under the
Possibility of Multiple Infectors
Doroudi Sherwin1 , Kang Kang
1 - University of Minnesota [Twin Cities] (United States)

Abstract: Mathematical models of disease transmission in small scale setting typically focus on static
environments with a limited number of individuals. Recent work inspired by the ongoing COVID-19
pandemic has considered disease transmission in environments that behave like queueing models (i.e.,
where individuals arrive and depart over time). One such framework builds upon the exponential dose
response model in order to extend the epidemiological concept of the basic reproduction rate to the"system-specific basic reproduction rate." This system-specific metric is defined as the expected number
susceptible individuals that a single infectious individual will infect during a single visit to a queueing
system, under the single-infector assumption that all other individuals in the system are susceptible. Our
work expands this framework by consider multiple-infector models, which relax the assumption that at
most one individual is infectious, and account for the fact that susceptible individuals are more at risk
when in the presence of a greater number of infectious individuals. While the analysis of single-infector
models is often simplified by employing the linearity of expectation, multiple-infector models require
nuanced analysis that takes into account a variety of correlations. Using a variety of queueing-theoretic
techniques to overcome these challenges, we derive expressions for the probability that a susceptible
individual becomes infected in a variety of multiple-infector models, including those built upon the M/M/1(under both FCFS and PLCFS scheduling orders), M/M/1/k, and M/M/infinity, queueing systems; weshow that these results are consistent with existing results for the system-specific base reproduction
rate in limiting regime where the probability that an individual is infectious tends to zero. Our findings
enable to us to assess interventions and identify settings where it is necessary to consider multiple-infector
models.

3.6.5

Invited session T3-5 (Penington): Branching processes and their applications (Room 104)

Thursday 29th, 15:30 - 17:00 - chaired by Sarah Penington
Gonzalez Casanova Adrian: The speed of (a variant of ) Muller’s ratchet can be calculatedusing multi-type logistic branching processes
Gonzalez Casanova Adrian1 , Smadi Charline2 , Wakolbinger Anton3
1 - UC Berkeley and UNAM (Mexico)
2 - Grenoble (France)
3 - University of Frankfurt (Germany)

Abstract: Consider a population of N individuals, each of them carrying a type in N0 . The population
evolves according to a Moran dynamics with selection and mutation, where an individual of type k has
the same selective advantage over all individuals with type k ′ > k, and type k mutates to type k + 1
at a constant rate. This model is thus a variation of the classical Muller’s ratchet: there the selective
advantage is proportional to k ′ − k. For a regime of selection strength and mutation rates which is
between the regimes of weak and strong selection/mutation, we obtain the asymptotic rate of the clicktimes of the ratchet (i.e. the times at which the hitherto minimal best’ type in the population is lost),
and reveal the quasi-stationary type frequency profile between clicks. The large population limit of thisprofile is characterized as the normalized attractor of a “dual” hierarchical multitype logistic system, andalso via the distribution of the final minimal displacement in a branching random walk with one-sided
steps. An important role in the proofs is played by a graphical representation of the model, both forward
and backward in time, and a central tool is the ancestral selection graph decorated by mutations.
Hartung Lisa: Systems of F-KPP equations
Hartung Lisa1
1 - JGU Mainz (Germany)

Abstract: In this talk I will give an overview on the conncetion between (systems of) F-KPP equations
and spatial branching processes. Then I explain how probabbilistic tools can be used to analyse thesolutions to these equations. The talk is based on joint work with A. Bovier

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Pain Michel: The height of weighted recursive trees
Pain Michel1 , Sénizergues Delphin2
1 - Université de Toulouse (France)
2 - Université Paris Nanterre - Département de Mathématiques et Informatique (France)

Abstract: Weighted recursive trees are built by adding successively vertices with predetermined weights
to a tree: each new vertex is attached to a parent chosen at random with probability proportional
to its weight. I will present results obtained with Delphin Sénizergues concerning the height of thesetrees. When the total weight of the tree grows polynomially, we show that the height is in the same
universality class as the maximum of supercritical branching random walks. When the total weight
grows sub-polynomially, universality is broken and we exhibits several new regimes.
Villemonais Denis: Some asymptotic results for discrete time weighted branching particle
systems
Villemonais Denis1
1 - Institut Élie Cartan de Lorraine (France)

Abstract: Discrete time weighted branching particle systems are branching processes where each offspringis attributed a weight and a type, and where each generation can be composed of countably infinitely
many individuals with a finite total weight. During this talk, I will present time asymptotic results forthese processes.

3.6.6

Invited session T3-6 (Wang): Queues, bandits, and reinforcement
learning (Room 105)

Thursday 29th, 15:30 - 17:00 - chaired by Weina Wang
Agrawal Shubhada: CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption
Agrawal Shubhada1 , Mathieu Timothee2 , Basu Debabrota2 , Maillard Odalric-Ambrym2
1 - School of Industrial and Systems Engineering [Georgia Tech] (United States)
2 - Scool (France)

Abstract: We study the regret-minimization problem in the multi-armed bandit setting with unboundedstochastic corruption, where the agent might not always observe the reward generated from the selectedarm but instead sees a sample generated from an arbitrary corruption distribution with potentiallyunbounded support. The amount of corruption is controlled by a corruption proportion ε ∈ (0, 12 ). In thistalk, we will first develop a generic problem-dependent lower bound on the regret that holds for a given
family of reward distributions with arbitrary corruption distributions. For the setting with Gaussianarm-distributions with known variance, we will then see an asymptotically-optimal algorithm (CRIMED)
matching the lower bound exactly. Notably, CRIMED is the first algorithm that can handle corruption
levels up to 1/2. We also provide numerical results showing that CRIMED significantly outperforms theexisting algorithms for this setting.
You Wei: Information-Directed Selection for Top-Two Algorithms
You Wei1 , Qin Chao2 , Wang Zihao1 , Yang Shuoguang1
1 - the Hong Kong University of Science and Technology (Hong Kong SAR China)
2 - Columbia University (United States)

Abstract: We consider the best-k-arm identification problem for multi-armed bandits, where the objective is to select the exact set of k arms with the highest mean rewards by sequentially allocating
measurement effort. We characterize the necessary and sufficient conditions for the optimal allocation
using dual variables. Remarkably these optimality conditions lead to the extension of top-two algorithm
design principle (Russo, 2020), initially proposed for best-arm identification. Furthermore, our optimality
conditions induce a simple and effective selection rule dubbed information-directed selection (IDS) that
selects one of the top-two candidates based on a measure of information gain. As a theoretical guarantee,
we prove that integrated with IDS, top-two Thompson sampling is (asymptotically) optimal for Gaussian
best-arm identification, solving a glaring open problem in the pure exploration literature (Russo, 2020).
As a by-product, we show that for k > 1, top-two algorithms cannot achieve optimality even with anoracle tuning parameter. Numerical experiments show the superior performance of the proposed top-two

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algorithms with IDS and considerable improvement compared with algorithms without adaptive selection.

3.6.7

Invited session T3-7 (Backhausz):(Room 106)

Random graphs and matrices

Thursday 29th, 15:30 - 17:00 - chaired by Agnes Backhausz
Holmgren Cecilia: The asymptotic distribution of cluster sizes for supercritical percolation
on random split trees
Holmgren Cecilia1 , Berzunza Gabriel2
1 - Uppsala Universitet [Uppsala] (Sweden)
2 - University of Liverpool (United Kingdom)

Abstract: We consider the model of random trees introduced by Devroye (1998), the so-called random
split trees. The model encompasses many important randomized algorithms and data structures. We thenperform supercritical Bernoulli bond-percolation on those trees and obtain the asymptotic distribution forthe sizes of the largest clusters. Gabriel Berzunza, Cecilia Holmgren, Random Structures and Algorithms,
2022
Mathien Joffrey: Diameter of random surfaces built from random graphs.
Mathien Joffrey1
1 - Aix-Marseille Université - Faculté des Sciences (France)

Abstract: The study of random surfaces, especially in the asymptotic of large genus, is of increasing
interest in recent years. Many geometrical questions have analogous formulations in the theory randomgraphs with a large number of vertices, and results obtained in one domain can inspire the other. In thisway, we get interested in the diameter of random surfaces, which is a basic measure of the geometry of
the surface. In 2019, Budzinski, Curien and Petri studied this measure for a special model of surfaces,
built from random graphs. We extend it to obtain a richer class of models of random surfaces and wecompute the asymptotic of the diameter of these surfaces. The strategy of the proof relies on a detailed
study of an exploration process which is the analog to the breadth-first search exploration of a randomgraph. Its analysis is notably based on subadditive and concentration techniques.
Müller Noela: The rank of sparse symmetric matrices over arbitrary fields
Müller Noela1 , Van Der Hofstad Remco1 , Zhu Haodong1
1 - Eindhoven University of Technology (Netherlands)

Abstract: The talk discusses a combinatorial approach to study the asymptotic normalised rank of sparse
random matrices with arbitrary edge weights. Here, the edge weights can be assigned from an arbitrary
field, e.g. a finite field, and thus also the ensuing rank is considered with respect to that field. The
presented result shows that for a mildly general class of sparse symmetric matrices, the asymptotics ofthe normalised rank are independent of potential edge weights in the graph and the field, in the sense thatthe limiting constant of the normalised rank coincides with previously established results for symmetricmatrices over the reals with 0/1-entries. The underlying proof is based on an intricate extension of thenovel perturbation approach from Coja-Oghlan, Ergür, Gao, Hetterich and Rolvien to the symmetricsetting.
Shneer Seva: First passage percolation on Erdos-Renyi graphs with general weights
Daly Fraser1 , Schulte Matthias2 , Shneer Seva1
1 - Heriot-Watt University [Edinburgh] (United Kingdom)
2 - Technical University Hamburg Harburg [Hamburg] (Germany)

Abstract: We consider an Erdos-Renyi random graph on n nodes where the probability of an edge being
present between any two nodes is equal to λ/n with λ > 1. Every edge is assigned a (non-negative)
weight independently at random from a general distribution. For every path between two typical vertices
we introduce its hop-count (which counts the number of edges on the path) and its total weight (whichadds up the weights of all edges on the path). We prove a limit theorem for the joint distribution of
the appropriately scaled hop-count and general weights. This theorem, in particular, provides a limiting
result for hop-count and the total weight of the shortest path between two nodes. This is a joint work

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with Fraser Daly and Matthias Schulte.

3.6.8

Invited session T3-8 (Comte): Online stochastic matching: a diversityof viewpoints (Room 202)

Thursday 29th, 15:30 - 17:00 - chaired by Céline Comte
Jonckheere Matthieu: Online matching for the multitype stochastic block model
Jonckheere Matthieu1 , Moyal Pascal, Soprano Loto Nahuel
1 - CNRS (France)

Abstract: We consider the problem of sequential matching in a stochastic block model (SBM) withseveral types of nodes and generic compatibility constraints. When the probabilities of connections do
not scale with the size of the graph, we show that under the NCOND condition, a simple max-weightpolicy allows to attain an asymptotically perfect matching while no sequential algorithm attain perfectmatching otherwise. The proof relies on a specific Markovian representation of the dynamics associatedwith Lyapunov techniques.
Kerimov Süleyman: Near-Optimal Policies for Dynamic Matching
Kerimov Süleyman1 , Ashlagi Itai2 , Gurvich Itai3
1 - Rice University (United States)
2 - Stanford University (United States)
3 - Northwestern University (United States)

Abstract: We study centralized dynamic matching markets with finitely many agent types and heterogeneous match values. An inherent trade-off arises between short- and long-term value. A social
planner may delay match decisions to thicken the market and increase match opportunities to generatehigh value. This inevitably compromises short-term objectives, and the planner may match greedily tomaximize short-term value. A matching policy is hindsight optimal if the policy can (nearly) maximize
the total value simultaneously at all times. We first establish that in multi-way networks, where a match
can include more than two agent types, a simple periodic clearing policy with a carefully chosen period
length is hindsight optimal. Interestingly, in two-way networks, where any match includes two agent
types, suitably designed greedy policies also achieve hindsight optimality.
Reiffenhäuser Rebecca: Online Max-Weight Matchings with Limited Priors
Reiffenhäuser Rebecca1 , Caramanis Constantine2 , Lazos Philip3 , Fusco Federico4 , Dütting Paul5 , Faw
Matthew2 , Leonardi Stefano6 , Papadigenopoulos Orestis7 , Pountourakis Emmanouil8
1 - Universiteit van Amsterdam (Netherlands)
2 - University of Texas at Austin [Austin] (United States)
3 - Input Output (United Kingdom)
4 - Sapienza - University of Rome (Italy)
5 - google research (Switzerland)
6 - Sapienza University of Rome (Italy)
7 - Columbia University (United States)
8 - Drexel University (United States)

Abstract: When matchings are computed online, access to distributional information has a huge impact
on the achieved competitive guarantees. This talk explores a setting with a minimal amount of suchknowledge, i.e. single-sample prophet inequalities, and gives small constant-factor approximations fordifferent settings, including that of matching strategic agents to goods, as well as discussing extensions
to combinatorial auctions.
Zubeldia Martin: Matching Queues with Abandonments in Quantum Switches: Stability
and Throughput Analysis
Zubeldia Martin1 , Jhunjhunwala Prakirt2 , Maguluri Siva Theja2
1 - University of Minnesota [Twin Cities] (United States)
2 - Georgia Institute of Technology [Atlanta] (United States)

Abstract: Inspired by quantum switches, we consider a discrete-time multi-way matching system withtwo classes of arrivals: requests for entangled pair of qubits between two nodes, and qubits from each nodethat can be used to serve the requests. An important feature of this model is that qubits decohere and soCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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abandon over time. In contrast to classical server-based queueing models, the combination of queueing,
server-less multi-way matching, and abandonment make the analysis a challenging problem. The primary
focus of this paper is to study a simple system consisting of two types of requests and three types of qubits
operating under a Max-Weight policy. In this setting, we characterize the stability region under the MaxWeight policy by adopting a two-time scale fluid limit to get a handle on the abandonments. In particular,
we show that Max-Weight is throughput optimal and that it can achieve throughputs larger than theones that can be achieved by non-idling policies when the requests are infinitely backlogged. Moreover,despite the use of the Max-Weight policy, we show that there can be a counter-intuitive behavior in thesystem: the longest requests queue can have a positive drift for some time even if the overall system isstable.

3.6.9

Invited session T3-10 (Decreusefond): Stein method II (Room 204)

Thursday 29th, 15:30 - 17:00 - chaired by Laurent DecreusefondHalconruy Hélène: Stochastic analysis for marked binomial processes and Poisson approximations
Halconruy Hélène1
1 - ESILV (France)

Abstract: Clumps can be observed by studying the number of occurrences of a rare word in a DNA
sequence. The Chen-Stein method provides tools to bound the error by approximating the distribution of
the number of clusters by a Poisson law (possibly compound). In the work that supports this presentation,
we propose to revisit Chen-Stein method by reducing the initial problem to that of a compound Poisson
approximation for functionals of binomial marked processes (MBPs). Those are discrete analogues ofcompound Poisson processes for which we develop stochastic analysis tools as well as a Malliavin calculus
based on a family of operators (gradient, divergence, etc.). Under this new formalism, we obtain a generalcriterion - for the total variation distance - of the poissonian approximation for MBP functionals in termsof Malliavin operators. In this talk, I will present elements of the Malliavin calculus for MBPs, before
stating the general approximation result and illustrating it to study the distribution of a rare word in a
DNA sequence.
Peccati Giovanni: Stein’s method and quantitative two-scale stabilization
Peccati Giovanni1
1 - University of Luxembourg [Luxembourg] (Luxembourg)

Abstract: I will discuss some bounds on the normal approximation of (possibly multidimensional) functionals of Poisson random measures, implicitly using the geometric notion of "two-scale stabilization".
These estimates can be used to deduce explicit estimates on the rate of convergence of geometric functionals of random point configurations, in situations (related e.g. to problems of combinatorial optimization)
where iterated add-one cost operators (and, consequently, second-order Poincaré inequalities) are notamenable to analysis.
Swan Yvik: Normal approximation for the posterior in exponential families
Swan Yvik1
1 - Université Libre de Bruxelles (Belgium)

Abstract: We obtain quantitative Bernstein-von Mises type bounds on the normal approximation of theposterior distribution in exponential family models when centering either around the posterior mode or
around the maximum likelihood estimator. Our bounds, obtained through a version of Stein’s method,
are non-asymptotic, and data dependent; they are of the correct order both in the total variation and
Wasserstein distances, as well as for approximations for expectations of smooth functions of the posterior.
All our results are valid for univariate and multivariate posteriors alike, and do not require a conjugate
prior setting. We illustrate our findings on a variety of exponential family distributions, including Poisson,
multinomial and normal distribution with unknown mean and variance. The resulting bounds have an
explicit dependence on the prior distribution and on sufficient statistics of the data from the sample,
and thus provide insight into how these factors may affect the quality of the normal approximation. The
performance of the bounds is also assessed with simulations.

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Decreusefond Laurent: Stein’s method for discrete alpha stable point processes
Decreusefond Laurent1 , Vasseur Aurélien1
1 - Télécom Paris (France)

Abstract: Many probability distributions satisfy some stability properties (alpha-stable distribution andamong them, the Gaussian distribution, Poisson distribution, max-stable, etc.) We are here interested ina class of point processes introduced by Davydov et al. which obeys a stability with respect to thinning
operations. We define a Ornstein-Uhlenbeck like semigroup thanks to this property and then show how toconstruct a Malliavin derivative. We can then the usual machinery of Stein-Malliavin calculus to derivesome new convergence rates for stable-CLTs.

3.7

Friday 30th, 10:30 - 12:00

3.7.1

Invited session F1-1 (Robert): Stochastic Models of Biology (Auditorium)

Friday 30th, 10:30 - 12:00 - chaired by Philippe Robert
Zaherddine Jana: Stochastic Models of Regulation of Gene Expression in Biological Cells
Zaherddine Jana1
1 - Inria (France)

Abstract: Gene expression is a fundamental process in cellular life. It is a two step process: Transcription(production of RNAs) and Translation (production of proteins). We study an important regulation
mechanism of transcription of bacterial cells using specific macro-molecules, 6S RNAs. The functional
property of these 6S RNAs is of blocking the transcription of RNAs when the cell environment is notfavorable to its growth. In this talk, after a brief introduction, we will investigate the efficiency of thismechanism with a stochastic model in different growth regimes of the cell. The results are mainly basedon the proof of averaging principles for the associated Markov processes. The consequences of theseresults are discussed.
Vignoud Gaetan: Spike-timing dependent plasticity in stochastic neuronal networks
Vignoud Gaetan1,2,3
1 - Dioxycle (France)
2 - Inria de Paris (France)
3 - Centre interdisciplinaire de recherche en biologie (France)

Abstract: In neuroscience, synaptic plasticity refers to the set of mechanisms driving the dynamics ofthe connections between neurons. These synapses and their evolution have a great influence on learningand memory, and can be represented by a scalar value, the synaptic weight. We will consider a stochastic
system with two connected neurons, with a variable synaptic weight that depends on point processes
associated to each neuron. The input neuron spikes are generated by an homogeneous Poisson process,while the output neuron has a spiking intensity that depends on the jumps of the input neuron andthe synaptic weight. A scaling regime where the rate of both point processes is large compared to the
dynamics of the synaptic weight corresponds to a classical assumption in computational neuroscience, andoffers interesting properties. Using this stochastic averaging principle, we will present different regimesof the synaptic weight dynamics for various plasticity rules, with both analytical and numerical results.
Véber Amandine: Growth properties of the infinite-parent spatial Lambda-Fleming-Viotprocess
Véber Amandine1
1 - MAP5, CNRS, Université Paris Cité (France)

Abstract: The infinite-parent spatial Lambda-Fleming-Viot process is a model for spatially expanding
populations in a two dimensional continuum, in which empty areas are filled with ghost individuals.
This model can be seen as a continuous-space version of the Eden growth model, and it comes with adual process that allows us to trace back the origins of a sample of individuals taken from the current

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population. In this talk, we shall focus on the growth properties of the area covered by real individuals.
With the help of a simple toy model, we shall also investigate how the fluctuations at the front edge
lead to a much larger speed of growth of the occupied region than that predicted by simple first-moment
estimates. Joint work with Apolline Louvet (University of Bath).
Campillo Fabien: Nonlinear filtering in neurosciences
Campillo Fabien1
1 - Equipe MODEMIC INRA/INRA (UMR MISTEA, 2 Place Pierre Viala, Bat 29, Montpellier, France France)

Abstract: Nonlinear filtering is a set of tools that allow to identify, in a dynamic and recurrent way, ahidden component from observations in a Markov context. Does this take a particular form in neuroscience?

3.7.2

Invited session F1-2 (Stolletz): Stochastic modeling of manufacturing
and service operations (Room 101)

Friday 30th, 10:30 - 12:00 - chaired by Raik Stolletz
Karabag Oktay: Production and Energy Mode Control of a Production-Inventory System
Karabag Oktay1,2 , Tan Barıs3 , Khayyati Siamak4
1 - Yaşar University (Turkey)
2 - Erasmus University Rotterdam (Netherlands)
3 - Koç University (Turkey)
4 - University of Mannheim (Germany)

Abstract: Energy efficiency in manufacturing can be improved by controlling energy modes and production dynamically. We examine a production-inventory system that can operate in Working, Idle, andOff energy modes with mode-dependent energy costs. There can be a warm-up delay to switch from one
mode to another. With random inter-arrival, production, and warm-up times, we formulate the problem
of determining in which mode the production resource should operate at a given time depending on thestate of the system as a stochastic control problem under the long-run average profit criterion considering
the sales revenue together with energy, inventory holding and backlog costs. The optimal solution to the
problem for the exponential inter-arrival, production, and warm-up times is determined by solving theMarkov Decision Process with a linear programming approach. The structure of the optimal policy forthe exponential case uses two thresholds to switch between the Working and Idle or Working and Off
modes. We use the two-threshold policy as an approximate policy to control a system with correlated
inter-event times with general distributions. This system is modeled as a Quasi Birth and Death Process
and analyzed by using a matrix-geometric method. Our numerical experiments show that the joint production and energy control policy performs better compared to the pure production and energy control
policies depending on the system parameters. In summary, we propose a joint energy and production
control policy that improves energy efficiency by controlling the energy modes depending on the state of
the system.
Khayyati Siamak: A Hybrid Approximation Method for Time-Dependent Queues in Service
Systems
Khayyati Siamak1 , Foroughi Amir, Stolletz Raik1
1 - University of Mannheim (Germany)

Abstract: Many service systems have time-dependent parameters e.g. queues in healthcare facilities andcall centers. For this reason, analysis of time-dependent queues is of great interest. Among the various
available approximation methods for time-dependent queues, the fluid/diffusion-based methods, and thestationary-backlog-carryover (SBC) method are efficient and accurate. However, the fluid/diffusion basedmodels do not perform well for under-loaded periods and the SBC method’s performance is negatively
affected by the over-loaded periods. To remedy these drawbacks, we develop a hybrid method thatalternates between the two methods aiming to apply each one when they perform best. Finally using
numerical experiments, we show the accuracy and the efficiency of the hybrid method.

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Schwarz Justus Arne: A production planning model for non-stationary and stochastic yieldin the semiconductor industry
Schwarz Justus Arne1 , Diefenbach Johannes2 , Karaesmen Fikri3 , Stolletz Raik2
1 - University of Regensburg (Germany)
2 - Universität Mannheim [Mannheim] (Germany)
3 - Koç University (Turkey)

Abstract:
In the semiconductor industry, the yield of a production process tends to start low but
increases with cumulative production volume when a new product or machine is introduced. Due to longproduction lead times, the company has to choose the production quantity before observing the realized
yield. We formalize the firm’s problem as a newsvendor problem with stochastic and non-stationary
yield and deterministic demand. We derive analytical and numerical insights into the optimal quantity
to produce and the expected profit. For stationary yield, we characterize the optimal production by acritical-fractile and discuss the sensitivity of the results to non-stationary yield. A numerical study showsthat the optimal production quantity using the proposed model is close to the ex-post optimal production
quantity for real yield data.
Rochlin Igor: The Impact of Outdated Information and the Optimal Up- date Policy
Rochlin Igor1 , Kerner Yoav1 , Roet-Green Ricky2
1 - Ben-Gurion University of the Negev (Israel)
2 - University of Rochester [USA] (United States)

Abstract: Many services, such as emergency rooms, border-crossing agencies and airports provide wait
time information to their customers. However, the information is not updated continuously, due tolimitations of the wait-time tracking technology. We propose a model in which the information regarding
the system’s state is not updated continuously. We assume that the provider shares accurate information
with their customers. The updating process follows an endogenous Poisson process. Customers makejoining decisions knowing that the information might be outdated. They use the posted information toinfer their expected waiting time in the system. Our model bridges between the classical observable andunobservable queueing models: if the time between updates goes to zero, the queue becomes observable; if
the time between updates goes to infinity, the queue becomes unobservable. In our research, we calcualted
the expected waiting given the outdated queue-length information. It followed by a proof of the uniquenss
of equlibirum strategy of the customers as a function of the update rate and system’s parameters. We
then show some numerical examples of system’s performence measures: throughput and social welfare.
In most cases, as expected, either the fully observable or the unobservable model, is optimal. However,there are cases in which the throughput and/or the social welfare are unimodal functions with respect tothe update rate. We conclude with the question whether there is value of sharing the update age withthe customers

3.7.3

Invited session F1-3 (Henderson): Healthcare (Room 102)

Friday 30th, 10:30 - 12:00 - chaired by Shane Henderson
Chick Stephen: Adaptive Sequential Clinical Trials for Precision Medicine with Predictive
and Prognostic Covariates
Chick Stephen1 , Alban Andres2 , Zoumpoulis Spyros1
1 - INSEAD (France)
2 - Massachussetts General Hospital (United States)

Abstract: Motivated by work with collaborators at a university medical center, we sought to develop
techniques improve the rate of learning in clinical trials for precision medicine. In such applications,
there may be structural knowledge as to which covariates might react with various treatments in thetrial, and other covariates that may affect outcomes independent of treatment. We extend the expectedvalue of information/knowledge gradient approach to account for such covariate structures, differencesfrom LASSO-type approaches, and illustrate computational results for a realistic example.
Van Der Mei Rob: Reducing waiting times by coordination in complex acute healthcare
systems
Van Der Mei Rob1,2
1 - Centrum Wiskunde & Informatica (P.O. Box 94079 1090 GB Amsterdam Netherlands Netherlands)
2 - VU University Amsterdam (Faculty of Sciences De Boelelaan 1081 1081 HV Amsterdam The Netherlands Netherlands)

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Abstract: In many countries, the current system of acute care (e.g., elderly care, youth care, mental
healthcare) is not functioning optimally. As a consequence, patients-in-need often experience majordifficulties in receiving prompt, adequate and appropriate care when really needed. Temporary care is forexample not always available, patients who cannot go straight-home after a hospital admission are often
difficult to place, and the hospital’s emergency department is structurally overloaded. These problems
typically arise from a lack of coordination in today’s complex system of healthcare at the macro level,
and from an interest bottleneck’ between the different stakeholders involved. In this talk, I will show how
quantitative models and methods can make the difference.
Henderson Shane: Community First Responders: Recruitment Guidelines and Dispatch
Henderson Shane1 , Van Den Berg Pieter2 , Fourmentraux Oceane3 , Jagtenberg Caroline3 , Li Hemeng(maggie)4
1 - Cornell University [New York] (United States)
2 - Erasmus University Rotterdam (Netherlands)
3 - Vrije University (Netherlands)
4 - Cornell University (United States)

Abstract: Patient survival from out-of-hospital cardiac arrest (OHCA) can be improved by augmenting
traditional ambulance response with the dispatch of community first responders (volunteers) who arealerted via an app. How many volunteers are needed, from where should volunteers be recruited, andhow should they be dispatched? We use a combination of Poisson point process modeling and convex
optimization to address the first two questions; the right areas from which to recruit are not always
obvious, because volunteers recruited from one area may spend time in various areas across a city. We
use a combination of dynamic programming and decision trees to answer the last question, balancing
the goal of a fast response to the current patient with the need to avoid disengagement of volunteers
that arises when multiple volunteers respond. A case study for Auckland, New Zealand demonstrates theideas.

3.7.4

Contributed session F1-4 (Wang): Networks and optimization (Room103)

Friday 30th, 10:30 - 12:00 - chaired by Jinting Wang
Lim Andrew: Mechanisms for Coordinating Systems of Decentralized Agents
Lim Andrew1
1 - National University of Singapore (Singapore)

Abstract: We consider a decentralized stochastic service system where arrival rates and service rates arecontrolled by different agents. Not surprisingly, the aggregate system is not efficient if agents optimize
individual costs in isolation, so the challenge is to come up with incentives under which the collection ofdecentralized decisions optimizes the system. We characterize transfer payments between agents underwhich the collection of decentralized decisions coincides with the centralized optimal, and propose adecentralized algorithm for computing the optimal transfers. The transfer payments are related to the
VCG mechanism from mechanism design, and the algorithm for computing them is incentive compatible
and does not require the services of a mechanism designer to be implemented. Convergence and robustness
properties of both mechanisms will be discussed.
Avramidis Thanos: Dynamic Pricing with Finite Price Sets via Upper Confidence Bounds
Avramidis Athanasios (thanos)1
1 - School of Mathematics, University of Southampton (United Kingdom)

Abstract: We study price optimization of perishable inventory over multiple, consecutive selling seasonsin the presence of demand uncertainty. Each selling season consists of a finite number of discrete time
periods, and demand per time period is a Bernoulli random variable with price-dependent parameter.
The set of feasible prices is finite, and the expected demand (Bernoulli parameter) corresponding toeach price is unknown to the seller, whose objective is to maximize cumulative expected revenue. Under
full information (that is, upfront knowledge of the Bernoulli parameters), an optimal pricing policy isdetermined by a dynamic program with finite horizon and finite state and action sets. In the absence ofsuch knowledge, we propose a pricing algorithm such that in each season the pricing policy is determinedas the solution to the revised dynamic program where the Bernoulli parameters are replaced by respective(current) upper confidence bounds. Revenue performance is measured by the regret: the expected revenue

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loss relative to the optimal attainable revenue under full information. With the number of seasons n
known upfront, our main result is an O(log(nlog(n))) upper bound on the regret. The analysis hinges
on concentration results (McDiarmid 1998, Cappe et al 2013). Numerical comparisons to alternatives
from the literature indicate the competitiveness of our approach. References McDiarmid, C. (1998),
Concentration, in Probabilistic methods for algorithmic discrete mathematics’, Springer, pp. 195-248.
CappÂťe, O., Garivier, A., Maillard, O.-A., Munos, R. and Stoltz, G. (2013), Kullback-leibler upper
confidence bounds for optimal sequential allocation’, The Annals of Statistics 41(3), 1516-1541.
Jiang Jiashuo: Degeneracy is OK: Logarithmic Regret for Network Revenue Managementwith Indiscrete Distributions
Jiang Jiashuo1 , Ma Will2 , Zhang Jiawei3
1 - Hong Kong University of Science and Technology (Hong Kong SAR China)
2 - Columbia University School of Business (United States)
3 - NYU-Stern School of Business (United States)

Abstract: We study the classical Network Revenue Management (NRM) problem with accept/rejectdecisions and T IID arrivals. We consider a distributional form where each arrival must fall under a finite
number of possible categories, each with a deterministic resource consumption vector, but a random value
distributed continuously over an interval. We develop an online algorithm that achieves O(log2 T ) regret
under this model, with no further assumptions. We develop another online algorithm that achieves animproved O(log T ) regret, with only a second-order growth assumption. To our knowledge, these are thefirst results achieving logarithmic-level regret in a continuous-distribution NRM model without further"non-degeneracy" assumptions. Our results are achieved via new techniques including: a new methodof bounding myopic regret, a "semi-fluid" relaxation of the offline allocation, and an improved bound on
the "dual convergence".
Wang Jinting: Value-at-Risk Based Queueing
Wang Jinting1 , Zhu Sheng2
1 - Central University of Finance and Economics (China)
2 - Henan Polytechnic University (China)

Abstract: The value-at-risk (VaR) based queueing economics in observable/unobservable queues withstrategic but non-risk-neutral customers is studied. Confidence level and risk tolerance value constitute
the VaR measure that is used to characterize customers’ risk preference in which homogeneous and heterogeneous cases are both studied. We find that the optimal strategies of non-risk-neutral customers arestill of threshold-type in the observable model, albeit in a quite different way compared with risk-neutral
customers. The individually optimal threshold is non-increasing in the confidence level and nondecreasingin the tolerance value. In the unobservable model, we obtain the one-dimensional (multi-dimensional)
equilibrium joining probability when customers have the homogeneous risk preference (heterogeneousrisk preferences). From the viewpoint of social welfare maximization, in the case of homogeneous risk
preference, the socially optimal risk preference belongs to a “ribbon" region in the observable model,
while it forms a curve in the unobservable case. When customers have heterogeneous risk preferences,
the socially optimal confidence level is a point if the real-time queue length is unobservable, and it belongs
to a region if the real-time queue length is observable. We reveal that from the perspective of throughput
maximization, it is better not to disclose the real-time queue length information when the risk confidence
level is sufficiently low. From the perspective of social welfare, disclosing the real-time information is more
conducive to the social welfare in the case of homogeneous risk preference, while hiding the information
can gain more social welfare if customers are heterogeneous.

3.7.5

Invited session F1-5 (De Saporta): Learning while controling: crossroads between stochastic control and statistical learning (Room 104)

Friday 30th, 10:30 - 12:00 - chaired by Benoîte De Saporta
Le Quellennec Orlane: An example of medical treatment optimization under model uncertainty
Le Quellennec Orlane1 , De Saporta Benoîte1 , Cleynen Alice1 , Sabbadin Régis2
1 - IMAG, Univ Montpellier, CNRS, Montpellier, France (France)
2 - Univ Toulouse, INRAE-MIAT, Toulouse, France (France)

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Abstract: Human diseases such as cancer involve long-term follow-up. A patient alternates phases ofremission with relapses. A biomarker is monitored throughout the follow-up. Its dynamic is modeledby a controlled piecewise deterministic Markov process (PDMP). The PDMP evolves in continuous timeand space, the process is partially observed through noise and some of its parameters are unknown,
making the control problem especially difficult. To our knowledge, there is no method to control sucha PDMP, i.e. to maximize the life of the patient while minimizing the treatment cost and side effects.
First, we consider discrete dates only for the decisions, thus turning the controlled PDMP into a partiallyobserved Markov decision process (POMDP), with a continuous state space. Then, through simulations,
we compare Bayesian and non-Bayesian reinforcement learning methods to solve this POMDP.
Maillard Odalric-Ambrym: Regret minimization in MDPs: Lower bound-guided strategies.
Maillard Odalric-Ambrym1
1 - Inria Lille - Nord Europe (France)

Abstract: When considering Markov Decision Problem (MDP) from a learning perspective, the transitions and reward models are unknown from the agent that must interact with the environment in orderto receive observations (samples). We are interested in the problem of learning while controling an MDP,
that is when the learner interacts in a single-trajectory of state-action-rewards and the learner competes
with an omnisicent oracle perfeclty knowing the MDP and how to optimally control it. More precisely, we
consider the problem of regret minimization in a discrete, undiscounted, infinite-horizon Markov Decision
Problem (MDP) under the average reward criterion, and focus on the minimization of the regret withrespect to an optimal policy, when the learner does not know the rewards nor the transitions of the MDP.
Characterizing the optimal regret achievable in such MDPs is not trivial as it involves a complextity terms
written as an intricate optimization problem. In certain cases, this problem simplifies and can exploited
to guide the construction of a sound, efficient learning strategy. In this talk, we will shed a novel lighton regret minimization strategies, by extending to reinforcement learning the computationally appealing
Indexed Minimum Empirical Divergence (IMED) bandit algorithm. We will then discuss how this fruitfulidea can be modified to make computations of the index fully iterative, hence reducing computational
complexity while retaining sound guarantees. We will illustrate this promising strategy and how discuss
how they may permeate to revisit gradient descent approaches for learning in larger MDPs.
Pham Huyên: Policy gradient learning methods for stochastic control with exit time andapplications to share repurchase pricing
Hamdouche Mohamed1 , Henry-Labordere Pierre2 , Pham Huyên1
1 - Université Paris Cité (France)
2 - Qube RT (France)

Abstract: We develop policy gradients methods for stochastic control with exit time in a model-freesetting. We propose two types of algorithms for learning either directly the optimal policy or by learning
alternately the value function (critic) and the optimal control (actor). The use of randomized policies
is crucial for overcoming notably the issue related to the exit time in the gradient computation. We
demonstrate the effectiveness of our approach by implementing our numerical schemes in the application
to the problem of share repurchase pricing. Our results show that the proposed policy gradient methods
outperform PDE or other neural networks techniques in a model-based setting. Furthermore, our algorithms are flexible enough to incorporate realistic market conditions like e.g. price impact or transaction
costs.
Trottner Lukas: Learning to reflect - Data-driven solutions to singular control problems
Trottner Lukas1
1 - Aarhus University (Denmark)

Abstract: Even though theoretical solutions to stochastic optimal control problems are well understoodin many scenarios, their practicability suffers from the assumption of known dynamics of the underlying
stochastic process, raising the statistical challenge of developing purely data-driven strategies. For theclasses of continuous diffusion processes and Lévy processes, we show that developing efficient strategies
for related singular stochastic control problems can essentially be reduced to finding rate-optimal estimators with respect to the sup-norm risk of objects associated to the invariant distribution of ergodic
processes which determine the theoretical solution of the control problem. From a statistical perspective,
we exploit exponential mixing properties as the common factor of both scenarios to drive the convergence
analysis, indicating that relying on general stability properties of Markov processes is a sufficiently powerful and flexible approach to treat complex applications requiring statistical methods.

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105

Invited session F1-6 (Jonckheere): Probabilistic tools for learning(Room 105)

Friday 30th, 10:30 - 12:00 - chaired by Matthieu Jonckheere
Ferragut Andres: Spatial estimation of EV energy demand based on aggregated measurements
Ferragut Andres1 , Espindola Emiliano2
1 - Universidad ORT Uruguay (Uruguay)
2 - Universidad ORT Uruguay (Uruguay)

Abstract: Electrical vehicle (EV) adoption is currently growing exponentially, and may even speed up
once adoption prices become competitive against carbon based alternatives. However, EV charging is a
new energy and power intensive application for the electrical grid. Preparing and deploying the necessary
charging infrastructure is thus a challenging task, particularly in the grid last mile. In our work, wedevelop techniques to spatially estimate the amount of "miles" or "kilometers" that are currently required
from vehicles based on the total amount of gas sales in a given region. Our goal is to provide a suitable
geographic estimate of the energy density that needs to be fulfilled, in order to inform future investment
decisions for grid operators. Mathematically, the main challenge is to estimate a distributed density
function when measurements are only taken at certain points, e.g. gas sales at given gas stations. These
measurements correspond to integrals of the underlying density over a space partition. Given this input,
we propose two parametric models based on Gaussian Radial Basis Functions: one based on minimizing
mean square error and a further approach using maximum likelihood estimation, and discuss how to build
the estimators based on a gradient descent approach. We test the results on real data from California.
Jonckheere Matthieu: Metric Learning : Choosing the parameter of the Fermat distanceFerraris Laure1 , Chazal Frédéric1 , Groisman Pablo2 , Jonckheere Matthieu3 , Pascal Frederic4 , Sapienza
Facundo5
1 - Inria (France)
2 - Universidad de Buenos Aires (Argentina)
3 - CNRS (France)
4 - CentraleSupélec (France)
5 - UC Berkeley (United States)

Abstract: In this talk, I will present a "measure-dependent metric learning" problem. Inspired fromoptics, the Fermat distances have been recently established as a powerful tool for machine learning tasks.
Given a non-vanishing density function f on a domain or a submanifold in RD the Fermat distances
define the length of a shortest path between two points. The optimal path remains in the support of f
and travels faster into high density regions, where this latter measure-dependent effect is calibrated by aparameter α. Hence, the family of Fermat distances is able to capture both the geometry of the support
and the underlying density. Considering the estimator of the Fermat distance inferred from a dataset, I
will focus on the choice of the parameter α. This problem turns out to be crucial for the performanceof the subsequent tasks. In the context of applications to clustering, i will show by a combination oftheoretical and heuristic arguments that there is a window of useful α, while a well chosen parameterscales with the dimension of the data. This work is a collaboration with Frédéric Chazal, Pablo Groisman,
Matthieu Jonckheere, Frédéric Pascal and Facundo Sapienza.
Mastropietro Daniel: Fleming-Viot particle systems as a mechanism to improve learningfrom rare events
Mastropietro Daniel1,2 , Ayesta Urtzi2,3,4,5 , Jonckheere Matthieu5,6 , Majewski Szymon7
1 - Institut National Polytechnique (Toulouse) (France)
2 - Institut de recherche en informatique de Toulouse (France)
3 - Ikerbasque - Basque Foundation for Science (Spain)
4 - University of the Basque Country/Euskal Herriko Unibertsitatea (Spain)
5 - Laboratoire dánalyse et dárchitecture des systèmes (France)
6 - Institut de recherche en informatique de Toulouse (France)
7 - Département de Mathématiques de l’École polytechnique (France)

Abstract: In this talk we introduce Fleming-Viot particle systems as a mechanism to improve explorationand learning from rare events in contexts where importance sampling is not an option. Queueing systems
serve as illustration, where the objective is to learn the optimum acceptance policy of incoming jobs ofdifferent classes in terms of lost opportunity costs due to rejection. Policy gradient methods are used

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to learn the optimum integer-valued thresholds that are a function of the queue occupancy. Learning
happens by observing the cost of rejecting incoming jobs of each of all possible classes which, dependingon the thresholds, can occur very rarely. Fleming-Viot particle systems leverage information about thestructure of the queueing system in order to boost the observation of these rejection events. This gives
rise to the FVRL algorithm that combines Fleming-Viot with Reinforcement Learning to efficiently learn
the optimum thresholds. A Monte-Carlo-based reinforcement learning method is used as benchmark forcomparison.
Keppo Jussi: Diversified Learning: Bayesian Control with Multiple Biased Information
Sources
Keppo Jussi1,2
1 - Xinyuan Zhang (Canada)
2 - Michael Jong Kim (Canada)

Abstract: We consider a decision-maker (DM) who can sample from multiple information sources to learn
the underlying state before making an earning decision. The DM optimizes her sampling and earning
decisions to maximize her payoffs. The problem is motivated by financial and healthcare applications
with multiple information sources. We develop a Bayesian control framework for such problem and an
efficient algorithm to solve it. Furthermore, we solve the problem explicitly in estimation and testing
settings.

3.7.7

Invited session F1-7 (Hachem): Large random matrices and statisticallearning (Room 106)

Friday 30th, 10:30 - 12:00 - chaired by Walid Hachem
Fan Zhou: Random linear estimation with rotationally-invariant designs: Asymptotics athigh temperature
Fan Zhou1 ,Yufan Li, Subhabrata Sen, Yihong Wu
1 - Yale University (United States)

Abstract: We study estimation in the linear model, in a Bayesian setting where the signal has an entrywise
i.i.d. prior and the design is rotationally-invariant in law. In the large system limit as dimension andsample size increase proportionally, a set of related conjectures have been postulated for the asymptotic
mutual information, Bayes-optimal mean squared error, and TAP mean-field equations that characterize
the Bayes posterior mean of the true signal. In this work, we prove these conjectures for a general classof signal priors and for arbitrary rotationally-invariant designs, under a "high-temperature" condition
that restricts the range of eigenvalues of the design. Our proof uses a conditional second-moment method
argument, where we condition on the iterates of a version of the Vector AMP algorithm for solving the
TAP mean-field equations.
This is joint work with Yufan Li, Subhabrata Sen, and Yihong Wu
Loubaton Philippe: Testing that a large number of time series are independent using linear
spectral statistics of the estimated spectral coherence matrix
Loubaton Philippe1
1 - Université Paris-Est-Marne La Vallée (France)

Abstract: We address the problem of testing that M jointly stationary complex gaussian times series(y1,n )n∈Z , . . . , (yM,n )n∈Z are independent when the number of time series M is large and that the numberof available observations N is not unlimited. We model this general context by the asymptotic regime
where M = M (N ) verifies C1 N α ≤ M ≤ C2 N α where C1 > 0 and C2 > 0 are two constants and whereα < 1. In order to propose relevant test statistics, we consider the frequency smoothed estimated spectral

−1/2

−1/2
coherence matrix ĈN (ν) defined at each frequency ν by ĈN (ν) = Diag(ŜN (ν))
ŜN (ν) Diag(ŜN (ν))
where ŜN (ν) is the frequency smoothed estimated spectral density matrix of the M -variate time sePB/2
1∗
ries (yn = (y1,n , . . . , yM,n )T )n∈Z defined by ŜN (ν) = B+1
b=−B/2 ξ y (ν + b/N )ξ y (ν + b/N ) withPN
ξ y (ν) = √1N n=1 yn e−2iπ(n−1)ν . Assuming that the smoothing span B = B(N ) verifies cN = M
B →c, 0 < c < 1 when N → +∞, the properties of ĈN (ν) under the hypothesis H0 that the M timeseries are independent were studied in [1]. It was in particular shown that the empirical eigenvalue

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distribution of ĈN (ν) converges almost surely towards the Marcenko-Pastur distribution µc with parameter c. Therefore, if ϕ is a smooth function, the linear spectral statistics ϕ̂N (ν) of ĈN (ν) defined byRPM
1
1ϕ̂N (ν) = M
Tr ϕ(ĈN (ν)) verifies ϕ̂N (ν) − ϕ(λ)dµcN (λ) → 0. [1] also studied
m=1 λm (ĈN (ν)) = M
R
the rate of convergence of ϕ̂N (ν) − ϕ(λ)dµcN (λ). In this presentation, we take benefit of the results ofk[1] to establish that for a well chosen frequency grid EN included into {RN , k = 0, . . . , N
 − 1}, then, unP
der the hypothesis H0 , the integrated statistics ζN = ν∈EN ϕ̂N (ν) − ϕ(λ)dµcN (λ) converges, afterconvenient recentering and renormalization, towards a standard gaussian random variable. This allows
to evaluate the asymptotic type I error of the corresponding statistical test. [1] P. Loubaton, A. Rosuel,“Properties of linear spectral statistics of frequency-smoothed estimated spectral coherence matrix ofhigh-dimensional Gaussian time series”, Electronic J. of Statistics, vol. 15, no. 2, pp. 5380-5454, 2021.
Joint work with Alexis Rosuel and Pascal Vallet.
Maillard Antoine: Some advances on extensive-rank matrix denoising and factorization
Maillard Antoine1
1 - ETH Zurich (Switzerland)

Abstract: Factorization of matrices where the rank of the two factors diverges linearly with their sizes
has many applications in diverse areas such as unsupervised representation learning, dictionary learningor sparse coding. We consider a setting where the two factors are generated from known component-wise
independent prior distributions, and the statistician observes a (possibly noisy) component-wise functionof their matrix product. In the limit where the dimensions of the matrices tend to infinity, but theirratios remain fixed, we expect to be able to derive closed form expressions for the optimal mean squared
error on the estimation of the two factors. However, this remains a very involved mathematical andalgorithmic problem. A related, but simpler, problem is extensive-rank matrix denoising, where one
aims to reconstruct a matrix with extensive but usually small rank from noisy measurements. In thistalk we will start by approaching the latter problem, and relate previously-known rotationally-invariant
estimators to Bayes-optimal estimation, and show how exact asymptotic calculations of the minimal error
can be performed using extensive-rank Harish-Chandra-Itzykson-Zuber integrals. We will then highlightthe additional difficulties posed by the matrix factorization setting, and will layout a systematic way
to derive the corrections to existing approximations, using high-temperature expansions at fixed orderparameters which originated in statistical physics.
Stephan Ludovic: Sparse random hypergraphs: Non-backtracking spectra and community
detection
Stephan Ludovic1 , Zhu Yizhe2
1 - Ecole Polytechnique Fédérale de Lausanne (Switzerland)
2 - University of California [Irvine] (United States)

Abstract: We consider the community detection problem in a sparse q-uniform hypergraph G, assumingthat G is generated according to the Hypergraph Stochastic Block Model (HSBM). We prove that aspectral method based on the non-backtracking operator for hypergraphs works with high probability
down to the generalized Kesten-Stigum detection threshold conjectured by Angelini et al. (2015). We
characterize the spectrum of the non-backtracking operator for the sparse HSBM and provide an efficient
dimension reduction procedure using the Ihara-Bass formula for hypergraphs. As a result, community
detection for the sparse HSBM on n vertices can be reduced to an eigenvector problem of a 2n × 2n
non-normal matrix constructed from the adjacency matrix and the degree matrix of the hypergraph. To
the best of our knowledge, this is the first provable and efficient spectral algorithm that achieves theconjectured threshold for HSBMs with r blocks generated according to a general symmetric probability
tensor.

3.7.8

Invited session F1-8 (Perchet): Online and dynamic matchings (Room202)

Friday 30th, 10:30 - 12:00 - chaired by Vianney Perchet
Aouad Ali: A Nonparametric Framework for Online Stochastic Matching with Correlated
ArrivalsAouad Ali1 , Ma Will2
1 - London Business School (United Kingdom)
2 - Columbia Business School (United States)

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Abstract: The design of online policies for stochastic matching and revenue management settings is
usually bound by the Bayesian prior that the demand process is formed by a fixed-length sequence ofqueries with unknown types, each drawn independently. This assumption of serial independence implies
that the demand of each type, i.e., the number of queries of a given type, has low variance and isapproximately Poisson-distributed. Thus, matching policies are often based on "fluid" linear programs
that only use the expectations of these distributions. This paper explores alternative stochastic models
for online matching that allow for nonparametric, higher variance demand distributions. We propose
two new models, INDEP and CORREL, that relax the serial independence assumption in different ways
by combining a nonparametric distribution for the demand with standard assumptions on the arrival
patterns – adversarial or random-order. In our INDEP model, the demand for each type follows anarbitrary distribution, while being mutually independent across different types. In our CORREL model,
the total demand follows an arbitrary distribution, and conditional on the sequence length, the type ofeach query is drawn independently. In both settings, we show that the fluid LP relaxation based on
only expected demands can be an arbitrarily bad benchmark for algorithm design. We develop tighter
LP relaxations for the INDEP and CORREL models that leverage the exact distribution of the demand,
leading to matching algorithms that achieve constant-factor performance guarantees under adversarial
and random-order arrivals. More broadly, our paper provides a data-driven framework for expressing
demand uncertainty (i.e., variance and correlations) in online stochastic matching models.
Lykouris Thodoris: Efficient Decentralized Multi-Agent Learning in Asymmetric Bipartite
Queueing Systems
Lykouris Thodoris1
1 - Massachusetts Institute of Technology (United States)

Abstract: Learning in multi-agent systems often poses significant challenges due to interference between
agents. In particular, unlike classical stochastic systems, the performance of an agent’s action is not drawn
i.i.d. from some distribution but is directly affected by the (unobserved) actions of the other agents. Thisis the reason why most collaborative multi-agent learning approaches aim to globally coordinate allagents’ actions to evade this interference. In this talk, we focus on agents in a decentralized bipartite
queueing system, where N agents request service from K servers. Prior decentralized approaches aim
to decentrally identify a globally coordinated schedule, which leads to various shortcomings: they are
restricted to symmetric systems, have performance that degrades exponentially in the number of servers,
require communication through shared randomness, two-way message passing or unique identifiers, or arecomputationally demanding. In contrast, we provide a low-complexity algorithm that is run decentrally
by each agent, avoids the shortcomings of "global coordination", and leads the queueing system to haveefficient performance in asymmetric bipartite queuing systems while also having additional robustness
properties. Paper information: The paper on which this talk is based is joint work with Daniel Freundand Wentao Weng and can be found here: https://arxiv.org/abs/2206.03324. The paper is currently
under major revision in Operations Research, a preliminary version appeared at COLT’22, and Wentao
was selected as a finalist in the Applied Probability Society student paper competition for this work.
Sentenac Flore: Online Matching in Bipartite Random Graphs
Sentenac Flore1
1 - CREST (France)

Abstract: Motivated by sequential budgeted allocation problems, we investigate online matching problems on classes of random graphs. In particular, we look at the so-called configuration model andgeometric random graphs. We estimate the competitive ratio of the simplest algorithm, GREEDY, byapproximating some relevant stochastic discrete processes by their continuous counterparts, that are solutions of an explicit system of partial differential equations. This technique gives precise bounds on theestimation errors, with arbitrarily high probability as the problem size increases. In particular, it allows
the formal comparison between different configuration models. We also prove that, quite surprisingly,
GREEDY can have better performance guarantees than RANKING, another celebrated algorithm foronline matching that usually outperforms the former.
Tercieux Olivier: Unpaired Kidney Exchange: Overcoming Double Coincidence of Wants
without Money
Tercieux Olivier1 , Akbarpour Mohammad, Combe Julien, He Yinghua, Hiller Victor, Shimer Rob
1 - PARIS SCHOOL OF ECONOMICS (France)

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Abstract: For an incompatible patient-donor pair, kidney exchanges often forbid receipt-before-donation(the patient receives a kidney before the donor donates) and donation-before-receipt, causing a doublecoincidence-of-wants problem. We study an algorithm, the Unpaired kidney exchange algorithm, which
eliminates this problem. In a dynamic matching model, we show that waiting time of patients under
the Unpaired is close to optimal and substantially shorter than widely used algorithms. Using a richadministrative dataset from France, we show that Unpaired achieves a match rate of 63 percent and an
average waiting time of 176 days for transplanted patients. The (infeasible) optimal algorithm is only
slightly better (64 percent and 144 days); widely used algorithms deliver less than 40 percent and at least
232 days. We discuss a range of solutions that can address the potential practical incentive challenges of
the Unpaired. In particular, we extend our analysis to an environment where a deceased donor waitlist
can be integrated to improve the performance of algorithms. We show that our theoretical and empiricalcomparisons continue to hold. Finally, based on these analyses, we propose a practical version of the
Unpaired algorithm.

3.7.9

Invited session F1-9 (Kazi-Tani): Recent advances in actuarial science(Room 203)

Friday 30th, 10:30 - 12:00 - chaired by Nabil Kazi-Tani
Ecoto Geoffrey: Forecasting the cost of drought events in France by super learning
Ecoto Geoffrey1,2
1 - Mathématiques Appliquées Paris 5 (France)
2 - Caisse Centrale de Réassurance (France)

Abstract: Recent years in France have been marked by the occurrence of intense drought events, in linewith the conclusions of numerous studies analyzing the impact of climate change on natural disasters interms of frequency and intensity. Confronted with the increased exposure of the insurance and reinsurance
market, the actuarial community needs to assess as acurately as possible the cost of these events. In thistalk, we present a new methodology to estimate the damage due to a geotechnical drought event inFrance. We develop a dedicated sequential super-learning algorithm, which is adapted to the spatiotemporal dependency structure at play in the data. A theoretical analysis of the procedure provides
interesting performance guarantees. The exploitation of the algorithm on the data available to CCR
yields very relevant results.
Goffard Pierre-Olivier: Stochastic models for blockchain analysis
Goffard Pierre-Olivier1
1 - Institut de Recherche en Mathématiques Avancées (France)

Abstract:
This talk focuses on the consensus protocol at the heart of blockchain systems. Simple
stochastic models are introduced to measure the efficiency, decentralization and security of a blockchain
systems. The model for efficiency is a single server queue with bulk service, the model for centralization isa simple Polya’s urn and the model for security is analysed using standard insurance risk theory tools.
Weber Stefan: Modeling and Pricing Cyber Insurance
Weber Stefan1
1 - House of Insurance, Leibniz Universität Hannover (Germany)

Abstract: The talk will discuss recent developments and current challenges in cyber risk and insurance.
Kazi-Tani Nabil: Stochastic differential Colonel Blotto games on graphs - Applications tocyber security
Hazzami Mohamed Amine1 , Kazi-Tani Nabil1 , Varma Vineeth Satheeskumar2
1 - Institut Élie Cartan de Lorraine (France)
2 - Centre de Recherche en Automatique de Nancy (France)

Abstract: We extend standard Colonel Blotto games to a dynamic setting, using the framework ofcontinuous-time two-player zero-sum stochastic differential games. The game is played on a graph,
where each edge represents a battlefield, and where players control the drift of their output process oneach battlefield, subject to a budget constraint. Using a dynamic programming approach, we explicitlyCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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characterize the Nash equilibrium strategies and the value function of the game, in terms of a HamiltonJacobi-Bellman PDE that admits a smooth solution. Our formulation is general enough to include various
rewards, allowing different interpretations of the game. In particular, we allow reward functionals withinteractions betwen the battlefields: for example, player 1, interpreted as a defender, maximizes the
expected algebraic connectivity associated to the subgraph given by the edges that she won, while player
2, the attacker, minimizes the same criteria. This is a joint work with Mohamed Amine Hazzami (IECL,
Univ. Lorraine) and Vineeth S Varma (CRAN, CNRS).

3.7.10Invited session F1-10 (Murthy): Advances in stochastic modelling for
decision-making under uncertainty (Room 204)

Friday 30th, 10:30 - 12:00 - chaired by Karthyek Murthy
Feng Yifan: Nested Elimination: A Simple Algorithm for Best-Item Identification from
Choice-Based Feedback
Yang Junwen1 , Feng Yifan1
1 - National University of Singapore (Singapore)

Abstract: We study the problem of best-item identification from choice-based feedback. In this problem,
a company sequentially and adaptively shows display sets to a population of customers and collects
their choices. The objective is to identify the most preferred item with the least number of samples
and at a high confidence level. We propose an elimination-based algorithm, namely Nested Elimination(NE), which is inspired by the nested structure implied by the information-theoretic lower bound. NE
is simple in structure, easy to implement, and has a strong theoretical guarantee for sample complexity.
Specifically, NE utilizes an innovative elimination criterion and circumvents the need to solve complex
combinatorial optimization problems. We provide an instance-specific and non-asymptotic bound on
the expected sample complexity of NE. We also show NE achieves high-order worst-case asymptotic
optimality. Finally, numerical experiments from both synthetic and real data corroborate our theoreticalfindings.
Deo Anand: Risk Averse Decision Making Using Tail Self-Similarity
Deo Anand1 , Murthy Karthyek2
1 - Indian Institute of Management Bangalore (India)
2 - Singapore University of Technology and Design (Singapore)

Abstract: In this talk, we attempt to address impractically large sample requirements in optimisation
formulations involving tail risk measures such as Conditional Value at Risk. Based on importance sampling, we propose a novel scheme for risk averse optimisation assuming access to the evaluations of theobjective loss, and the distribution of the underlying covariates. We demonstrate that our algorithm
requires exponentially fewer samples that plain sample averages as the problem is made harder by increasing the level of risk aversion to meet a particular accuracy requirement in the optimal objective
value. The algorithm itself is derived using notion of self-similarity in distribution tails - rare events happen in structurally similar (and most likely) ways. We leverage this observation to infer the distributionof samples in the target rare region by appropriately extrapolating representative but less rare samples
observed more frequently on data. Our algorithm is an improvement over the state of the art methods,
most of which either Fail to give any guarantees on the amount of sample complexity reduction obtainedor Have to be carefully tuned to the underlying loss and covariate distribution. Numerical experiments
validate our theoretical claims and showcase practical utility of the proposed methods.
Das Bikramjit: Aggregating heavy-tails: tracking large jumps from random sums to Lévy
processes
Das Bikramjit1 , Fasen-Hartmann Vicky
1 - Singapore University of Technology and Design (Singapore)

Abstract: The tail behavior of aggregates of heavy-tailed random vectors is known to be determinedby the so-called principle of “one large jump”. We establish that, in fact, a more general principle is
at play. Assuming that the random vectors are multivariate regularly varying (heavy-tailed) on various
subcones of the positive quadrant, first we show that their aggregates are also regularly varying (heavytailed) on these subcones. This allows us to approximate certain tail probabilities which were rendered
asymptotically negligible under classical regular variation, despite the “one large jump” asymptotics. We

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also discover that depending on the structure of the tail event of concern, the tail behavior of the aggregates may be characterized by more than a single large jump. Insurance risk processes have often been
represented using Levy processes. We establish a relationship between multivariate regularly varying
Levy processes and multivariate regular variation of its Levy measure on different subcones; this whichsimplifies computing certain ruin probabilities. We illustrate our results using simulated data. The talkis based on a joint work with Vicky Fasen-Hartmann.

3.8

Friday 30th, 13:30 - 15:00

3.8.1

Invited session F2-2 (Ravner): Statistical inference for queueing systems (Room 101)

Friday 30th, 13:30 - 15:00 - chaired by Liron Ravner
Bodas Shreehari: Parametric inference of the periodic arrival rate to a queue with unobserved balking
Bodas Shreehari1 , Mandjes Michel1 , Ravner Liron2
1 - University of Amsterdam [Amsterdam] (Netherlands)
2 - University of Haifa [Haifa] (Israel)

Abstract: Consider a multi-server queueing system in which arriving customers are provided information
about the delay they will experience. Based on this information, they decide to wait for service or leave
the system. While previous work considered the case of a constant arrival rate of potential customers,
here we assume that they arrive according to a time-dependent periodic arrival rate. Each customer
has a patience level sampled from a distribution. The objective is to estimate the arrival rate andpatience distribution parameters using only knowledge of the actual queue-length process over time, i.e.,
customers who decide not to join are not observed. We assume that the structure of the arrival rate
and the patience distribution are known beforehand, making this a parametric inference problem. We
devise an MLE procedure to estimate the parameters. For certain parametric assumptions, consistency
and asymptotic normality of the estimator is proved by breaking down the observed queue-length process
into distinct regeneration cycles to invoke i.i.d structure.
Bodas Tejas: Some recent results on the convert queueing problem
Bodas Tejas1 , Yardi Arti1
1 - IIIT Hyderabad (India)

Abstract: In the covert queueing problem, we consider a setup where server Bob is in agreement withcustomer Willie to serve his jobs exclusively and yet would like to admit non-Willie jobs covertly. Seeingthat the server is occasionally idle, Bob is tempted to allow a stream of non-Willie jobs to generate someextra revenue. However, Bob must do so covertly without Willie being able to detect the presence ofoutside traffic. As a part of the contract, Bob is typically obliged to provide Willie with some information
or statistic on the utilization of the server. Examples of such statistic could be the arrival and departure
times of Willie jobs, length of each busy session, the number of successive Willie jobs served in eachsession etc. Given the information provided by Bob, Willie can perform optimal hypothesis testing toinfer whether Bob is serving non-Willie stream of jobs or not. Thus Bob aims to insert stream of nonWillie jobs covertly such that Willie is not able to detect the presence of non-Willie traffic with highprobability, leading to the term covert queueing. In this talk, we will look at some recent results on the
covert queueing problem that consider both i.i.d and non-iid statistic and discuss some interesting future
directions.
Wichelhaus Cornelia: Nonparametric approaches for unreliable queueing systems
Wichelhaus Cornelia1
1 - Technical University of Darmstadt (Germany)

Abstract: In this talk we will present statistical approaches for unreliable queueing systems with servers
which are unreliable, i.e. the servers interrupt the service process from time to time and take a random vacation period. Based only on observations of the arrival and departure epochs of the customers(without the possibility of their correct matching) we obtain information about the unknown repair time

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distributions and the breakdown rates of the servers. Explicit constructions of the estimators are presented as well as their analytic performance. Decompounding techniques for random sums play a majorrole in the proofs. By simulation results we illustrate the practicability of our analysis.
Ravner Liron: Nonparametric estimation of the M/G/1 job-size distribution from discrete
workload observations
Ravner Liron1
1 - University of Haifa [Haifa] (Israel)

Abstract: Consider an M/G/1 queue with an unknown job-size distribution. The workload process isobserved according to an independent Poisson sampling process. A non-parametric estimator for the cdf
of the job-size distribution is constructed by first estimating the characteristic function and then applying
an inversion formula. The convergence rate of the asymptotic risk is shown to depend on the smoothnessclass of the underlying job-size distribution.

3.8.2

Invited session F2-3 (Hu):(Room 102)

Queueing theory and its applications

Friday 30th, 13:30 - 15:00 - chaired by Yue Hu
Ata Baris: Dynamic Control of MultiClass Queue in Halfin-Whitt Regime: A Computational Approach for High-Dimensional problems
Ata Baris1 , Harrison J. Michael2 , Kasikaralar Ebru3
1 - University of Chicago Booth School of Business (United States)
2 - Stanford University, Graduate School of Business (United States)
3 - Universiity of Chicago, Booth School of Business (United States)

Abstract: We consider a dynamic control problem motivated by call centers and study it in the HalfinWhitt regime. We propose an effective method for solving the resulting diffusion control problem in highdimensions. We illustrate the effectiveness of the solution in several test problems, including one designed
using a call center data set.
Castellanos Antonio: An Infinite Server Queueing Model for Incarceration Diversion Decisions
Castellanos Antonio1 , Shi Pengyi2 , Ward Amy R.1
1 - University of Chicago (United States)
2 - Purdue University (United States)

Abstract: Community-based incarceration diversion programs have been shown to be an effective strategy
to reduce recidivism (when formerly incarcerated individuals commit an offence and are re-incarcerated).
These programs have garnered much interest due to the high rate of recidivism in the United States,
where 58% of individuals released on parole were rearrested for a new offense within a two-year period.
In collaboration with a government agency in Illinois we develop a data driven framework for modelingincarceration diversion decisions, with an eye towards using our model to provide decision support.
Specifically, we model the system with a multi-class infinite server queue model. In our model arrivals
correspond to program referrals, service times to length of stay in the programs, and in-service reneging
to when a person recidivates. We address important questions, such as: How should the population be
split into distinct subgroups of interest? Is a Poisson arrival process consistent with the data, and, if so,should the rate be time-stationary or time varying? What distribution best models service times? We
end by discussing how to use our framework to explore the tradeoffs among admission policy, program
utilization, and recidivism.
Hu Yue: Optimal Scheduling of Proactive Service with Customer Deterioration and Improvement
Hu Yue1 , Chan Carri2 , Dong Jing2
1 - The University of Chicago (United States)
2 - Columbia University (United States)

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Abstract: Service systems are typically limited resource environments where scarce capacity is reserved
for the most urgent customers. However, there has been a growing interest in the use of proactive service
when a less urgent customer may become urgent while waiting. On one hand, providing service forcustomers when they are less urgent could mean that fewer resources are needed to fulfill their service
requirement. On the other hand, utilizing limited capacity for customers who may never need the service
in the future takes the capacity away from other more urgent customers who need it now. To understandthis tension, we propose a multi-server queueing model with two customer classes: moderate and urgent.
We allow customers to transition classes while waiting. In this setting, we characterize how moderate
and urgent customers should be prioritized for service when proactive service for moderate customers is
an option. We identify an index, the modified cµ/θ-index, which plays an important role in determining
the optimal scheduling policy. This index lends itself to an intuitive interpretation of how to balanceholding costs, service times, abandonments, and transitions between customer classes.
Daw Andrew: Asymmetries of Service: Interdependence and Synchronicity
Daw Andrew1 , Yom-Tov Galit2
1 - University of Southern California (United States)
2 - Technion - Israel Institute of Technology [Haifa] (Israel)

Abstract: We propose and analyze a Hawkes cluster model of service interactions that captures two(a)symmetries between the customer and agent: co-production vs self-production, synchrony vs asynchrony. We analyze the stochastic process through a novel decomposition in terms of objects fromprobabilistic combinatorics. This model reveals connection to the behavioral operations literature, suchas non-monotonic system performance from monotonic agent-load slowdown, which we prove will exist
under customer-agent asynchrony. Given this existence, we can prescribe an optimal concurrency, orlevel of multi-tasking, for the service agents. However, the efficiency of such operational improvements islimited by symmetry in interdependence: the more co-productive the service, the less opportunity there
is to improve.

3.8.3

Invited session F2-5 (Bhulai): Markov decision problems (Room 104)

Friday 30th, 13:30 - 15:00 - chaired by Sandjai Bhulai
Koopmans Camiel: Optimal service control and observation restoration decisions in Markovian queues with fallible sensors.
Kanavetas Odysseas1 , Koopmans Camiel1 , Spieksma Floske1
1 - Mathematical Institute, Leiden University (Netherlands)

Abstract: Markov decision processes (MDPs) have been a source of many techniques and models frequently used nowadays. This talk aims to show how to apply techniques stemming from MDPs to
Markovian single-queue models where service rate control is performed with fallible sensors. More specifically, “fallible”means that after a sensor failure the queue cannot be observed until it is repaired. Thisdiffers from noisy partially-observable MDPs. In practice, such a model is applicable to situations where
sensors break down and their repairs have to be optimized. We search for policies that minimize costsby specifying the service rate to use and by scheduling the repair of the sensor. One of the approaches isbased on the fact that cer- tain mathematical properties (e.g., monotonicity and convexity) are preserved
under Value Iteration, which allows us to establish certain (threshold) structures in optimal policies.
Singh Vikas Vikram: Joint chance-constrained Markov decision Processes
Singh Vikas Vikram1 , Venktesan Varagapriya1 , Lisser Abdel2
1 - Indian Institute of Technogy Delhi (India)
2 - Université Paris-Saclay (France)

Abstract: We consider a finite state-action uncertain constrained Markov decision process under discounted and average cost criteria. The running costs are defined by random variables and the transition
probabilities are known. The uncertainties present in the objective function and the constraints are modelled using chance constraints. We assume that the random cost vectors follow multivariate ellipticallysymmetric distributions and dependence among the random constraints is driven by a Gumbel-Hougaard
copula. We propose two second order cone programming problems whose optimal values give lower andupper bounds of the optimal value of the uncertain constrained Markov decision process. As an application, we study a stochastic version of a service and admission control problem in a queueing system. The
proposed approximation methods are illustrated on randomly generated instances of queueing control
problem.

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Spieksma Floske: The deviation matrix of a denumerable, multichain Markov process incontinuous time
Spieksma Floske1 , Avrachenkov Kostya2
1 - Universiteit Leiden (Netherlands)
2 - Inria Sophia Antipolis (France)

Abstract: The deviation matrix of a denumerable, irreducible non-explosive Markov process is wellunderstood. However, to our knowledge no results so far exist, when the irreducibility requirement is
dropped. In this talk, we will present necessary and sufficient conditions for the deviation matrix toexist in the case of a potentially infinite number of closed classes, and a non-closed subset of transientstates. Under mild conditions, the deviation matrix has an alternative representation based on rank one
perturbations of the generator of the Markov process. An analog of this representation has been studied
earlier by Kartashov [1] under stronger assumptions for an irreducible, discrete time queueing system.
We will illustrate the use of this formula by an application to a restart process. [1] N. V. Kartashov.
Strong stable Markov chains. VSP, 1996.
Bhulai Sandjai: Optimal stopping policies when assessing students’ mastery
Sapountzi Anni, Storm Jaap, Bhulai Sandjai1 , Meeter Martijn
1 - Vrije Universiteit, Amsterdam (Netherlands)

Abstract: An important challenge in assessments and testing of learning outcomes is estimating whether
an individual has attained mastery of a single skill on the basis of sequences of responses. It is nottrivial to obtain a reliable diagnosis of mastery with as few students’ responses as possible and provide
an adaptive length assessment in an online and personalized manner. We propose an approach to solvethat problem. Our solution consists of three components: (1) a Markov reward model in which thevalue function serves as a proxy for a student’s mastery level; (2) a reinforcement learning and machinelearning technique to evaluate the value function, and (3) stopping policies, based on the value function,that balance the accuracy and the efficiency of the mastery assessment. These policies are intuitive andare easy to use in practice by, e.g., teachers. Finally, we demonstrate the efficacy of these stopping
policies using synthetic and historical student response data. The policies enable the efficient assessmentof mastery across broad students’ mastery levels.

3.8.4

Contributed session F2-6 (Hautphenne): Advanced stochastic models(Room 105)

Friday 30th, 13:30 - 15:00 - chaired by Sophie Hautphenne
Khezeli Ali: An Improved Lower Bound on the Largest Common Subtree of Random LeafLabeled Binary Trees
Khezeli Ali1
1 - Inria de Paris (France)

Abstract: In some models of random combinatorial structures, an object of interest is the largest common
substructures of two samples. A well-studied example is the size of the largest increasing subsequence ina random permutation, which can be regarded as the largest common subset in which the permutationinduces the same total order as the identity permutation (or another random permutation). In the context
of trees, the largest common subtree has been proposed in biology as a measure of similarity between
two phylogenetic trees (also known as the maximum agreement subtree). With this motivations, someworks have studied the expected size of the largest common subtree of two independent random trees. In
this paper, we consider two random binary trees built on n common labeled leaves. It is known that the
expected size of the largest common subtree is of order between n0.366 and n1/2 . We improve the lower
bound to order n0.4464 by constructing a common subtree recursively and by proving a lower bound for
its asymptotic growth. The construction is a modification of an algorithm proposed by D. Aldous by
splitting the tree at the centroid and by proceeding recursively.
Ganguly Ankan: Hydrodynamic Limits of Interacting Particle Systems on Sparse Graphs
Ganguly Ankan1 , Ramanan Kavita2
1 - University of Pennsylvania (United States)
2 - Brown University (United States)

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Abstract: Consider an interacting particle system indexed by the vertices of a (possibly random) locallyfinite graph. Each particle takes values in a countable state space and evolves according to a pure jumpprocess whose jump intensities depend only on its own state and states of particles in its neighborhood.
Under mild conditions on the jump intensities, it is shown that if the sequence of interaction graphs
converges locally in probability to a limit graph that satisfies a certain finite dissociability property, thenthe corresponding sequence of empirical measures of the particle trajectories converges weakly to thelaw of the marginal dynamics at the root vertex of the limit graph. The proof of this hydrodynamic
limit relies on several auxiliary results of potentially independent interest. First, such interacting particle
systems are shown to be well-posed on (almost surely) finitely dissociable graphs, which include graphsof maximal bounded degree and any Galton-Watson tree whose offspring distribution has a finite first
moment. A counterexample is also provided to show that well-posedness can fail for dynamics on graphs
outside this class. Next, the dynamics on a locally convergent sequence of graphs are shown to converge
in the local weak sense to the dynamics on the limit graph when the latter is finitely dissociable. Finally,
the dynamics are also shown to exhibit an (annealed) asymptotic correlation decay property. This talkis based on joint work with Kavita Ramanan.
Note to the committee: the author believes this talk would pair well with the talk by Juniper Cocomello.
Mertz Laurent: A Piecewise Deterministic Markov Process approach modeling a dry friction
problem with noise
Mertz Laurent1
1 - City University of Hong Kong [Hong Kong] (Hong Kong SAR China)

Abstract: Understanding and predicting the dynamical properties of systems involving dry friction isa major concern in physics and engineering. It abounds in many mechanical processes, from the soundproduced by a violin to the screeching of chalk on a blackboard to human infant crawling dynamics andfriction-based locomotion of a multitude of living organisms (snakes, bacteria, scallops..) to the displacement of mechanical structures (building, bridges, nuclear plants, massive industrial infrastructures) underearthquakes and beyond. Surprisingly, even for low-dimensional systems, the modeling of dry friction in
the presence of random forcing has not been elucidated. In this work, we propose a piecewise deterministic Markov process approach modeling a system with dry friction including different coefficients for thestatic and dynamic forces. In this mathematical framework, we derive the corresponding Kolmogorov
equations to compute statistical quantities of interest related to the distributions of the static (sticked)
and dynamic phases. We show ergodicity and provide a representation formula of the stationary measureusing independent identically distributed portions of the trajectory (excursions). We also obtain deterministic characterizations of the Laplace transforms of the probability density functions of the durationsof the static and dynamic phases. In particular, the analysis of the power spectral density of the velocity
reveals a critical value of the noise correlation time below which the correlations of the dynamic behaviors
coincide with those of the white noise limit. The existence of such a critical value was already mentioned
in the physics literature [Geffert and Just, Phys. Rev. E. 95 062111 (2017)]. This is joint work with J.
Garnier and Z. Lu.
Hautphenne Sophie: Consistent estimation for population-size-dependent branching processes
Braunsteins Peter1 , Hautphenne Sophie2 , Minuesa Carmen3
1 - University of New South Wales (Sydney) (Australia)
2 - The University of Melbourne (Australia)
3 - Universidad de Extremadura - University of Extremadura (Spain)

Abstract: In this talk we consider population-size-dependent branching processes, which are stochastic
models designed to capture the dynamics of populations living in restricted habitats. We derive several
estimators for parameters in these processes and demonstrate that some of these estimators do not satisfy
the classical consistency property called C-consistency. This leads us to define a new type of consistency
called Q-consistency. We then investigate the circumstances when C-consistent estimators are preferable
to Q-consistent estimators. We illustrate our results on the Chatham Island black robin population.

3.8.5

Invited session F2-7 (Leskela): Network inference II (Room 106)

Friday 30th, 13:30 - 15:00 - chaired by Lasse Leskela

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Deka Prabhanka: PageRank Nibble on the sparse directed stochastic block model
Banerjee Sayan1 , Deka Prabhanka1 , Olvera-Cravioto Mariana1
1 - University of North Carolina, Chapel Hill (United States)

Abstract: We present results on community recovery in a sparse, directed Stochastic Block Model (dSBM)
where the identities of a positive proportion of seeds in one community is known. Our method is based on
the PageRank Nibble algorithm, which uses personalized PageRank and a simple threshold rule to label
vertices in the same community as the seeds. This method leverages information about the community
label of some vertices, a situation often encountered in practice. Moreover, since PageRank is a local
algorithm that can be computed in a distributed and asynchronous fashion, it is highly efficient even inlarge graphs. Our estimates on the probability of misclassification are derived from a tractable expression
for the limiting PageRank distribution. This distribution is in turn obtained by using a new local weaklimit for a very general class of inhomogeneous random digraphs, which allows us to characterize it via asystem of distributional fixed point equations.
Lelarge Marc: Graph matching: from fundamental limits to algorithms
Lelarge Marc1
1 - inria (France)

Abstract: Alignment of graphs consists in finding a mapping between the nodes of two graphs which
preserves most of the edges. For two correlated Erdös-Rényi, we present information-theoretic resultsin the sparse regime. We then propose an algorithm based on local comparisons and prove theoreticalguarantees. Finally, we show the empirical success of learning algorithms.
Sanders Jaron: Spectral norm bounds for, and singular value distributions of, block Markov
chainsSanders Jaron1 , Senen-Cerda Albert1 , Van Werde Alexander1
1 - Eindhoven University of Technology [Eindhoven] (Netherlands)

Abstract: A Block Markov Chain (BMC) is a Markov chain whose state space can be partitioned intoa finite number of clusters such that the transition probabilities only depend on the clusters. BMCs
thus serve as a model for Markov chains with communities. In this talk, I will share with you some of
our latest results on the spectrum of random matrices built from the sample path of a BMC. Specifically, for the centered, empirical frequency matrix of a BMC, we have quantified the asymptotic orderof the largest singular value [1] and established limiting laws for the singular value distributions [2]. The
former result holds even in sparse regimes when the sample path is relatively short. [1] Jaron Sanders,
Albert Senen-Cerda (2022). Spectral norm bounds for block Markov chain random matrices. Stochastic
Processes and their Applications. https://doi.org/10.1016/j.spa.2022.12.004 [2] Jaron Sanders, Alexander Van Werde (2023). Singular value distribution of dense random matrices with block Markovian
dependence. Stochastic Processes and their Applications. https://doi.org/10.1016/j.spa.2023.01.001
Zhang Anderson Ye: Fundamental Limits of Spectral Clustering in Stochastic Block Models
Zhang Anderson Ye1
1 - University of Pennsylvania [Philadelphia] (United States)

Abstract: We give a precise characterization of the performance of spectral clustering for community
detection under Stochastic Block Models by carrying out sharp statistical analysis. We show spectralclustering has an exponentially small error with matching upper and lower bounds that have the same
exponent, including the sharp leading constant. The fundamental limits established for the spectralclustering hold for networks with multiple and imbalanced communities and sparse networks with degrees
far smaller than log n. The key to our results is a novel truncated ℓ2 perturbation analysis for eigenvectors
and a new analysis idea of eigenvectors truncation.

3.8.6

Invited session F2-8 (Busic): Stochastic matching and applications(Room 202)

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Banerjee Siddhartha: Priorities and Quotas in Online Matching
Banerjee Siddhartha1 , Eichhorn Matthew1 , Kempe David2
1 - Cornell University (United States)
2 - USC (United States)

Abstract: In many applications such as rationing medical care and supplies, university admissions, andthe assignment of public housing, the decision of who receives an allocation can be justified by various
normative criteria (ethical, financial, legal, etc.). Such settings motivate a natural priority-respecting
allocation problem: several categories, each with a quota of interchangeable items, wish to allocate theitems among a set of agents. Each category has a list of eligible agents and a priority ordering over theseagents; agents may be eligible in multiple categories. The goal is to select a valid allocation: one that
respects quotas, eligibility, and priorities, and ensures Pareto efficiency. I will describe some recent workof ours, which provides a complete algorithmic characterization of all valid allocations. I will then show
how this characterization allows us to understand the strange complexity landscape of such problems, aswell as characterize optimal tradeoffs in dynamic priority-respecting allocation. In particular, in settings
where allocations must be made to agents arriving online via some stochastic process, we show that while
insisting on zero priority violations leads to an Ω(T ) loss in efficiency, one can design allocation policies
that ensure that the ex post sum of the efficiency loss and priority violations is O(1).
Comte Céline: Stochastic Dynamic Matching: A Mixed Graph-Theory and Linear-Algebra
Approach
Comte Céline1 , Mathieu Fabien2 , Bušić Ana3,4
1 - LAAS-CNRS (France)
2 - Laboratory of Information, Network and Communication Sciences (France)
3 - Inria (France)
4 - Département d’informatique de l’École normale supérieure (France)

Abstract:
Stochastic dynamic matching problems have recently drawn attention in the stochasticmodeling community due to their numerous applications, ranging from supply-chain management tokidney exchange programs. In this paper, we consider a matching problem in which items of different
classes arrive according to independent Poisson processes. Unmatched items are stored in a queue, andcompatibility constraints are described by a simple graph on the classes, so that two items can be matched
if their classes are neighbors in the graph. We analyze the efficiency of matching policies, not only interms of system stability, but also in terms of matching rates between different classes. Our results relyon the observation that, under any stable policy, the matching rates satisfy a conservation equation that
equates the arrival and departure rates of each item class. This presentation is based on a joint workwith Fabien Mathieu (LINCS) and Ana Bušić (Inria and PSL University).
Fourneau Jean-Michel: Performance Paradox of Dynamic Matching under Greedy Policies
Fourneau Jean-Michel1 , Busic Ana, Cadas Arnaud, Doncel Josu
1 - Université de Versailles St Quentin (France)

Abstract: In our previous work [CDFB21], we focus on the First Come First Match policy and we study
the performance paradox that arises when we increase flexibility in the matching model. More precisely,
we analyze whether adding an edge to a matching graph leads to an improvement of the expectationof the number of items in the system. The main result of [CDFB21] provides sufficient conditions forthe existence of the performance paradox: despite a new edge in the matching graph, the expectationof the total number of items increase. It also shows some numerical examples of such a paradox. These
conditions are related to the heavy-traffic assumptions in queueing systems. The intuition behind theperformance paradox existence result is that the performance paradox occurs when the added edge in thecompatibility graph disrupts the draining of a bottleneck. The main contribution of this work consists ofgeneralizing the performance paradox existence analysis of [CDFB21] to a wide range of matching policies
and explore the type of matching graph where such paradoxes occur. First we formally define a large
set of policies based on the arriving letter and the state of the system which is assumed to be a word.
Then we define greedy matching policies. For such a policy, one may have several possible deletions ofmatched letters associated with a distribution of probability. But only one letter will be deleted. FCMF,
Match the longest, Random selection, Priority are examples of such disciplines. We prove several results
about the existence of performance paradoxes for greedy disciplines for some family of graphs. Moreprecisely, we prove several results about the transfert of the paradox from one graph to another one, orfor one discipline to another one. Most of these results are based on Strong Aggregation of Markov chains
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conditions of our main theorem. We first consider a matching model with several saturated independent
set. Then, we study the performance paradox when the saturated independent set is not that of the
maximum cardinality and for a different family of arrival distribution than that of required in our main
theorem.
Moyal Pascal: Stability regions of systems with compatibilities, and ubiquitous measureson graphs
Begeot Jocelyn1 , Marcovici Irène1 , Moyal Pascal1
1 - Institut Élie Cartan de Lorraine (France)

Abstract: In many service systems, it is necessary to take into account the compatibility constraints
between users, or between supplies and demands, and so on. The stability region of such systems canthen be seen as a set of measures (representing the arrival flows of the various classes of users or supplies,
demands, etc.) on a graph (representing the compatibilities between those classes). In this paper, weshow that these ‘stabilizing’ measures can always be easily constructed as a simple function of a familyof weights on the edges of the graph. Second, we show that the latter measures always coincide withinvariant measures of random walks on the graph under consideration. Some arguments in the proofs
rely on the matching rates of specific stochastic matching models. As a by-product of these arguments
we show that, in several cases, the matching rates are independent of the matching policy, that is, therule for choosing a match between various compatible elements.

3.8.7

Invited session F2-9 (Carassus): Robust finance (Room 203)

Friday 30th, 13:30 - 15:00 - chaired by Laurence Carassus
Denis Laurent: Robust maximization of recursive utility with penalization on the model
Denis Laurent1
1 - Laboratoire Manceau de Mathématiques[Le Mans] (France)

Abstract: We show that robust control under volatility uncertainty in a continuous time setup allows fora dynamic description in terms of second order backward differential equations (2BSDE). The relevant
penalizing mechanism is modeled by the distance of any two stochastic volatility models. Apart fromunique existence of related utility processes, we establish the related Bellman-Equation.
Pergamenchtchikov Serguei: Robust portfolio optimisation for jump-diffusion market models with transaction costs
Pergamenchtchikov Serguei1 , Egorov Sergei1
1 - Laboratoire de Mathématiques Raphaël Salem (France)

Abstract: We consider optimal consumption and investment problems under logarithmic utilities forfinancial markets described by semi-martingales with jumps defined through Lévy processes. For thisproblem, we show the corresponding verification theorem and construct the optimal strategies in the
explicit form. Then, we apply these strategies to markets with transaction costs. Through the Leland- Lepinette approach we construct the strategies based on the finite number of portfolio revisions forwhich the objective function tends to its optimal value uniformly over the market distributions when thenumber of portfolio revisions tends to infinity, i.e. the proposed strategies are robust and optimal in theasymptotic setting.
Wiesel Johannes: The Uniform Diversification Strategy Is Optimal for Expected Utility
Maximization under High Model Ambiguity
Wiesel Johannes1 , Carassus Laurence
1 - Columbia University (United States)

Abstract: In a one-period model with d risky assets, we investigate an expected utility maximization
problem when there is ambiguity on the probability P modeled by a Wasserstein ball of radius k around P.
We show that when k goes to infinity, the optimal solutions tend to some (rescaled) uniform diversification
strategy.

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Carassus Laurence: Quasi-sure essential supremum and applications to finance
Carassus Laurence1,2
1 - Resarch Center Pôle Léonard de Vinci (France)
2 - Laboratoire de Mathématiques de Reims (France)

Abstract: We place ourselves in the so-called quasi-sure setting where the unique probability measureprevailing on the state space is replaced by a set of measures which is neither supposed to be dominated,
nor supposed to be compact. A notion of essential supremum for a family of real-valued random variables
is developed in terms of upper semi-analytic functions. We show that this notion is indeed well-suitedfor financial problems as super-replication or Absence of Instantaneous Profit (AIP).

3.8.8

Invited session F2-10 (Rhee): Rare Events: Large Deviations, Simulation, and AI (Room 204)

Friday 30th, 13:30 - 15:00 - chaired by Chang-Han Rhee
Murthy Karthyek: Letting the samples speak: A new approach towards efficient ImportanceSampling for tail events
Deo Anand1 , Murthy Karthyek2
1 - Indian Institute of Management, Bangalore (India)
2 - Singapore University of Technology and Design (Singapore)

Abstract: The ability to estimate and control tail risks, besides being an integral part of quantitative risk
management, is central to running operations requiring high service levels and cyber-physical systems withhigh-reliability specifications. Despite this significance, scalable algorithmic approaches have remained
elusive: This is due to the rarity with which relevant risky samples get observed, and the critical role
experts play in devising variance reduction techniques based on instance-specific large deviations studies.
Our goal in this talk is to examine if such tailored variance reduction benefits can be instead achieved byinstance-agnostic algorithms capable of scaling well across multitude of tail estimation and optimisation
tasks. To this end, we identify an elementary transformation whose push-forward automatically induces
efficient importance sampling distributions across a variety of models by replicating the concentration
properties observed in less rare samples. This obviates the need to explicitly identify a good change ofmeasure, thereby overcoming the primary bottleneck in the use of importance sampling beyond highly
stylized models. Our novel approach is guided by a large deviations principle which brings out thephenomenon of self-similarity of zero variance distributions. Being a nonparametric phenomenon, this selfsimilarity phenomenon is manifest in a rich set of objectives modeled with tools such as linear programs,
piecewise linear/quadratic objectives, feature maps modeled with neural networks, etc. together with aspectrum of light and heavy-tailed multivariate distributions.
Wang Xingyu: Eliminating Sharp Minima from SGD with Truncated Heavy-tailed Noise
Wang Xingyu1 , Oh Sewoong2 , Rhee Chang-Han1
1 - Northwestern University (United States)
2 - University of Washington (United States)

Abstract: The empirical success of deep learning is often attributed to the stochastic gradient descent(SGD) algorithm’s mysterious ability to avoid sharp local minima in the loss landscape, as sharp minima
are known to lead to poor generalization. Recently, evidence of heavy-tailed gradient noise was reported
in many deep learning tasks, and Simsekli et al. (2019) argued that SGDs are capable of escaping sharp
local minima under the presence of such heavy-tailed gradient noise. This suggests a new promising
perspective to the mystery, but when heavy-tailed gradient noises are present, the pure form of SGD
often fails to train DNNs altogether, and one needs to deploy a variant of SGD, where gradients aretruncated above a fixed threshold. In this talk, we analyze the global dynamics of such a variation (often
called gradient clipping) and show that they are fundamentally different from those of the pure form ofSGD. In fact, this variant attains a strong notion of avoiding sharp minima: it can effectively eliminate
sharp local minima entirely from its training trajectory. We prove this by rigorously establishing an
Eyring-Kramers-type first exit time formula and metastability analysis for heavy-tailed SGDs: We reveal
the large deviations in heavy-tailed SGDs and obtain a sharp characterization of the relationship between
the first exit time from a local basin and the ratio between the truncation threshold and the width of
the basin. Moreover, when the objective function satisfies appropriate structural conditions, we provethat the heavy-tailed truncated SGD converges to a continuous-time Markov chain that never visits any
sharp minima as the step size decreases. Real data experiments on deep learning confirm our theoreticalprediction that heavy-tailed SGDs with gradient clipping find flatter local minima and achieve better
generalization.

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Zhe Su: Sample path large deviations for Levy processes and random walks with log-normal
increments
Zhe Su1 , Rhee Chang-Han1 , Zwart Bert2
1 - Northwestern University [Evanston] (United States)
2 - Centrum Wiskunde & Informatica (Netherlands)

Abstract: The past two decades have seen breakthroughs in understanding the general principle that
dictates the asymptotics of the rare events that arise in heavy-tailed systems. Hult, Lindskog, Mikosch,and Samorodnitsky (2005) was the first systematic confirmation of long-standing folklore in the heavy-tailliterature called the principle of a single big jump: that is, the systemwide rare events are often driven by
the catastrophic failure of a single component of the system when the underlying uncertainties are heavytailed, in particular, regularly varying. More recently, Rhee, Blanchet, and Zwart (2019) characterized
the ’catastrophe principle’ in the form of sample path large deviations, which addresses a more generalclass of heavy-tailed rare events far beyond the principle of a single big jump-i.e., the rare events thatare driven by an arbitrary number of unusually large shocks. Bazhba, Blanchet, Rhee, and Zwart(2020)
further extended the large deviations approach to the processes with (heavy-tailed) Weibul increment
distributions, showing that a version of the catastrophe principle still holds in such systems, but theexact characterization is more subtle. Among the three most popular classes of distributions-regularlyvarying, Weibull, and log-normal-for modeling heavy-tailed phenomena, this leaves only the log-normalcase open. In this talk, we establish the sample path large deviations for random walks and Lévy processes
and characterize the catastrophe principle for the log-normal case. We also discuss its implications in the
tail asymptotics of queueing systems with log-normal job sizes.
Rhee Chang-Han: From heavy-tailed large deviations to local stability analysis to global
dynamics
Rhee Chang-Han1 , Zwart Bert2 , Chen Bohan, Bazhba Mihail3 , Blanchet Jose4 , Seo Insuk5 , Ryu Jeeho5
1 - Northwestern University [Evanston] (United States)
2 - Centrum Wiskunde & Informatica (Netherlands)
3 - University of Amsterdam [Amsterdam] (Netherlands)
4 - Stanford University (United States)
5 - Seoul National University [Seoul] (South Korea)

Abstract: Stochastic dynamical systems often exhibit some form of local stability (metastability) andphase transition from a domain of local stability (a metastable set) to another domain of local stability.
For example, under the presence of multiple modes in the training landscape, stochastic gradient descent(SGD) can be attracted to and "stabilizes" at a suboptimal local minimum for a long time, even if itstrue stationary distribution has little mass at the local minimum and is predominantly concentrated
elsewhere. Such phenomena has a close connection to the large deviations of small-noise dynamicalsystems: If one considers a finite time horizon, escaping from a local minimum is a large deviation event.
On the other hand, if one waits for a long enough time, the SGD is guaranteed to escape any local minima
and eventually find the global minimum.
In this talk, we propose a new local uniform version of the heavy-tailed large deviations formulation and introduce the notion of asymptotic atom. We then show that one can transform such large
deviations formulation to a sharp local stability analysis (i.e., convergence of exit time and location distributions). These machineries provide a streamlined framework for the heavy-tailed counterpart of theclassical Freidlin-Wentzel theory and Eyring-Kramers formula which are limited to the light-tailed contexts. Moreover, we show that, due to the generality and precise nature of the asymptotics we develop,
the analysis can be further elevated to a crisp characterization of the global dynamics of the stochastic
systems. We illustrate this in the context of heavy-tailed SGDs and show that our framework successfully
captures intricate mathematical structures that is unique to the heavy-tailed SGDs.

3.9

Friday 30th, 15:30 - 17:00

3.9.1

Invited session F3-1 (Lelarge): Graph matching and planted models
in random graphs (Auditorium)

Friday 30th, 15:30 - 17:00 - chaired by Marc Lelarge

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Araya Valdivia Ernesto: Seeded graph matching under the Correlated Gaussian Wigner
Model
Braun Guillaume, Tyagi Hemant, Araya Valdivia Ernesto1
1 - Inria Lille - Nord Europe (France)

Abstract: Graphs are a convenient way to represent relational datasets. It has been widely used invarious domains such as computer vision, biology, and sociology. In many applications, one needs tofind a correspondence between these graphs. This problem is known as the graph matching problem.
For example, in computer vision, identifying key points of an object in pictures of the same object indifferent positions is an important problem. In this work, we focus on the case where some partial andnoisy knowledge about the correspondence between two graphs is available. We propose a refinement
algorithm, based on the generalized power method, that iteratively refines the initial correspondence
between the two graphs. We show that the algorithm, when properly initialized, provably converges afterO(log n) iterations, under the Correlated Gaussian Wigner Model, a classical generative model for dense
and weighted graphs. We complement our theoretical results with numerical experiments on synthetic
and real datasets showing that using this refinement algorithm can considerably improve the accuracy ofthe obtained matching.
Ganassali Luca: Aligning graphs via detecting correlation in trees
Ganassali Luca1
1 - EPFL (Switzerland)

Abstract:
Graph alignment refers to recovering the underlying vertex correspondence between two
random graphs with correlated edges. This problem can be viewed as an average-case and noisy versionof the well-known graph isomorphism problem. For correlated Erdös-Rényi random graphs, we will first
give insights on the fundamental limits for the planted formulation of this problem, establishing statisticalthresholds for partial recovery. Then, motivated by designing an efficient (polynomial-time) algorithm
to recover the underlying alignment in a sparse regime, a message-passing algorithm based on testingcorrelation in trees is proposed. We study this related correlation detection problem in trees and identify
a phase transition in the limit of large degrees for the existence of suitable tests, hence giving insights
on the performance of the above method for our initial problem on graphs. Based on joint works withLaurent Massoulié, Marc Lelarge and Guilhem Semerjian.
Mao Cheng: Detection-Recovery Gap for Planted Dense Cycles
Mao Cheng1
1 - Georgia Institute of Technology (United States)

Abstract: Planted dense cycles are a type of latent structure that appears in many applications, such as
small-world networks in social sciences and sequence assembly in computational biology. We consider amodel where a dense cycle with expected bandwidth nτ and edge density p is planted in an Erdős–Rényigraph G(n, q). We characterize the computational thresholds for the associated detection and recovery
problems for the class of low-degree polynomial algorithms. In particular, a gap exists between the two
thresholds in a certain regime of parameters. For example, if n−3/4 ≪ τ ≪ n−1/2 and p = Cq = Θ(1) fora constant C > 1, the detection problem is computationally easy while the recovery problem is hard for
low-degree algorithms.
Yu Sophie: Random graph matching at Otter’s tree-counting threshold via counting chandeliers
Mao Cheng1 , Wu Yihong2 , Xu Jiaming3 , Yu Sophie4
1 - Georgia Institute of Technology (United States)
2 - Yale University (United States)
3 - Duke University (United States)
4 - Duke university [Durham] (United States)

Abstract:
Given a pair of graphs, the problem of graph matching refers to finding the underlying
vertex correspondence that maximally aligns the edges. This is a ubiquitous problem arising in a variety
of applications across diverse fields, such as network privacy, computational biology, computer vision,and natural language processing. Graph matching is an instance of the notoriously difficult quadratic
assignment problem (QAP), which is NP-hard to solve or approximate.

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Despite the worst-case computational hardness of QAP, I will present a computationally efficientgraph matching algorithm based on counting a special family of trees. When the two graphs are ErdösRényi random graphs with correlated edges through the hidden vertex correspondence, we show that
our algorithm correctly matches all but a vanishing fraction of vertices with high probability as soon
as the edge correlation exceeds the square root of Otter’s constant. Moreover, we further upgrade thealmost exact recovery to exact recovery whenever it is information-theoretically possible. This is thefirst polynomial-time algorithm that achieves exact and almost exact matching with an explicit constantcorrelation for both dense and sparse graphs.

3.9.2

Invited session F3-2 (Huang & Xie): Stochastic models and their application (Room 101)

Friday 30th, 15:30 - 17:00 - chaired by Junfei Huang & Jingui Xie
Gao Xuefeng: Asymptotically Optimal Control of Make-to-Stock Systems
Gao Xuefeng1 , Huang Junfei
1 - The Chinese University of Hong Kong [Hong Kong] (China)

Abstract: We consider multi-class make-to-stock production/inventory systems in which the manager
makes three decisions, including pricing, outsourcing, and scheduling, to maximize the long-run average
profit. For a sequence of systems in the heavy-traffic regime, with linear or strictly convex holding/waiting
cost functions, we propose a sequence of policies and establish its asymptotic optimality. Our proof
combines the lower bound approach and a thorough steady-state analysis of the systems. We also establish
general results on the existence and tightness of the stationary distributions of the state processes undera more general family of policies.
Dong Jing: Scheduling queues with different and multiple resource requirements
Dong Jing1 , Chan Carri1 , Xi Weiye2
1 - Columbia University (United States)
2 - Tsinghua University (China)

Abstract: Motivated by the hospital inpatient setting, we study the scheduling of a new class of multiclass queueing systems with multiple types of resources where different classes of customers have heterogeneous - in terms of the type and amount - resource requirements. In particular, a customer may
require different numbers of "servers" from different server types to be allocated simultaneously in order
to be served. Some service capacity constraints cannot be violated while others can be violated witha penalty. We characterize a near-optimal scheduling policy. Our results provide insights into how thecapacity violation penalty affects the scheduling decision.
Shi Pengyi: Inpatient Overflow Management With Proximal Policy Optimization
Shi Pengyi1 , Sun Jingjing2 , Dai Jim3
1 - Purdue University [West Lafayette] (United States)
2 - CUHK-Shenzhen [Shenzhen] (China)
3 - Cornell University [Ithaca] (United States)

Abstract: One effective strategy for reducing congestion in hospitals is to overflow patients to nonprimary wards. However, this approach can lead to other issues such as mismatches in skillsets andcompromised quality of care. Therefore, it is necessary to balance the trade-off between congestion andoverflow costs. We model this overflow management problem as a discrete-time long-run average cost
Markov Decision Process, which have extremely large state and action spaces for systems with practicalrelevance. To overcome the curse-of-dimensionality, we develop a scalable actor-critic algorithm based on
Proximal Policy Optimization (PPO). Our approach incorporates two key innovations: (i) the integrationof atomic action and randomization to reduce the action space, and (ii) the use of a tailored timedependent approximation structure to estimate the advantage function. We show that our algorithm canhandle realistic problem sizes with over 20 server pools, overcoming the limitations of prior approaches.
Moreover, we show that our algorithm consistently outperforms state-of-the-art policies, and innovative
features in the algorithm design tailored to this domain application are essential for achieving superior
performance.

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Wang Zhengli: A Stochastic Control Framework for New Venture Creation
Wang Zhengli1 , Zenios Stefanos2
1 - The University of Hong Kong (Hong Kong SAR China)
2 - Stanford University (United States)

Abstract: We model the creation of a new venture with a novel stochastic control framework in which
the state of the venture is captured by a diffusion process. The entrepreneur creating the venture chooses
costly controls, which determine both the drift and the variance of the process. When the process reaches
an upper boundary, the venture succeeds and the entrepreneur receives a reward. When the processreaches a lower boundary, the venture fails. The entrepreneur can choose between different controls andwishes to determine the policy that maximizes the expected total reward minus total cost. We deriveclosed-form expressions under which the optimal policy will be dynamic versus static and characterize
the structure of the optimal policy.

3.9.3

Invited session F3-3 (Stolyar): Large-scale and heavy-traffic models(Room 102)

Friday 30th, 15:30 - 17:00 - chaired by Alexander Stolyar
Jhunjhunwala Prakirt: Rate of Convergence of Tail Probabilities to Heavy Traffic
Jhunjhunwala Prakirt1 , Hurtado-Lange Daniela2 , Maguluri Siva Theja3
1 - Georgia Institute of Technology (United States)
2 - William & Mary (United States)
3 - School of Industrial and Systems Engineering [Georgia Tech] (United States)

Abstract: A popular approach to computing performance measures of queueing systems (such as delay
and queue length) is studying the system in an asymptotic regime. However, these results are only valid
in the limit and often provide bounds for the pre-limit systems that are not optimized and, hence, giveloose bounds for the tail probabilities. In this work, we provide optimized bounds for the tail probabilitiesof the scaled total queue length in a load-balancing system under Join the Shortest Queue (JSQ). Our
bounds characterize the rate of convergence of the tail probabilities to the corresponding heavy trafficvalues. For the tail probability of the JSQ system, our bounds yield a multiplicative error that arises
from three factors: pre-limit errors, pre-exponent errors, and State-Space Collapse (SSC).
Mukherjee Debankur: A Stochastic Approximation Approach to Large-Scale Distributed
Rate Scaling
Mukherjee Debankur1 , Rutten Daan1 , Zubeldia Martin2
1 - School of Industrial and Systems Engineering [Georgia Tech] (United States)
2 - University of Minnesota (ISyE) (United States)

Abstract: In this talk, we will discuss a distributed rate-scaling mechanism for large-scale parallel-server
systems. The goal is to strike a delicate balance between the cost of maintaining the respective serverprocessing speeds and the average sojourn time of tasks in the entire system. The key challenge arises
from that servers need to dynamically scale their processing speeds in a fully distributed way without any
centralized control or exchange of information among the servers and global quantities like the arrivalrate, are unknown to the servers. Using insights from the stochastic approximation, we develop a novel
algorithm where the processing speed of each server converges to the globally optimum value in the steadystate as the system size becomes large. En route, we also analyze the performance of a fully heterogeneous
parallel-server system (i.e, each server has a different processing speed), which might be of independentinterest.
Zhong Yuan: Beating the curse of dimensionality in high-dimensional reflected diffusion
control problems
Zhong Yuan1
1 - University of Chicago (United States)

Abstract: Inspired by recent advances in efficient numerical schemes for high-dimensional partial differential equations, we develop a multi-level Monte Carlo Picard approximation scheme for a class ofreflected diffusion control problems. These problems arise as approximations to heavy-traffic diffusion
limits of parallel server systems, which have broad applications in service and technological systems. We

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show that the proposed numerical approximation scheme is provably efficient, in that the computational
effort grows at most polynomially in both the dimension of the state space and the inverse of the required
accuracy.
Stolyar Alexander: Data Flow Dissemination in a Network
Gopalan Aditya1 , Stolyar Alexander1
1 - University of Illinois at Urbana-Champaign (United States)

Abstract: We consider a network where multiple packet flows, originating in different nodes, need tobe disseminated to all other nodes. (This model is motivated, in particular, by blockchains and peer-topeer live streaming.) We show that Send-Random-Useful-Packet (RU) discipline is throughput optimal(has maximum stability region). Send-Oldest-Useful-Packet (OU) discipline is a natural candidate for
reducing the network Age-of-Information (AoI), i.e. the oldest packet age. We show that, surprisingly,
OU discipline is not throughput optimal. We prove that OU is throughput optimal in the important
special case of a complete-graph symmetric model. We also study the cumulative delays experienced bya packet as it propagates through the network, specifically their limiting behavior as the network size
becomes large.

3.9.4

Contributed session F3-4 (Remiche): Stochastic processes and Markov
modeling (Room 103)

Friday 30th, 15:30 - 17:00 - chaired by Marie-Ange Remiche
Yang Yunfang: Load Balancing in Geographically Separated Service Systems
He Shuangchi1 , Yang Yunfang1
1 - National University of Singapore (Singapore)

Abstract: Nowadays, smartphone apps allow customers to access real-time information (such as waitingtimes or queue lengths) about service facilities (e.g., clinics and bank branches). A customer may use
this information to decide where to go, if there are multiple facilities he can choose. If a facility is distantfrom the customer, the information may become obsolete when the customer arrives. It is known thatload balancing policies that rely on current information, such as the join-the-shortest-queue (JSQ) policy,
may induce queue length oscillations in the presence of information delays and seriously hurt the system
performance. In this work, we study a randomized variant of the JSQ policy for a service system withgeographically separated facilities in a region where customers randomly appear from different locations.
We prove that the proposed randomized policy is asymptotically optimal for minimizing a customer’stime to service (i.e., the sum of the traveling delay and the waiting time). We also establish a lower
bound for the time to service and demonstrate how to minimize the performance gap by fine-tuning therandomized policy.
Dendievel Sarah: Analysis of Singular Perturbation for a Markovian Fluid Model
Dendievel Sarah1,2
1 - Haute Ecole ICHEC - ECAM - ISFSC, Département ICHEC Brussels Management School, QUARESMI Laboratory(Belgium)
2 - Ghent University, TELIN Department, SMACS Research Group (Belgium)

Abstract: In this talk, we present a Markovian fluid model where the states are nearly completely
decomposable. Phases are thus organised in aggregates. The purpose of this research is to study thestationary density of the level of the complete model in the case of positive recurrent aggregates but also
to develop further analysis for the case when there are two aggregates with one being positive recurrent
and the other being transient. We provide some numerical example.
Ali Mohammadi Yeganeh: The Power of a Few Local Samples in Predicting Epidemics
Ali Mohammadi Yeganeh1 , Saberi Amin1 , Borgs Christian2 , Van Der Hofstad Remco
1 - Stanford (United States)
2 - UC Berkeley (United States)

Abstract: The spread of infectious diseases is heavily influenced by the interactions between individuals
in a population. Accurately mapping these interactions can be expensive and sometimes impossible,
making it difficult to predict the likelihood and outcome of an outbreak. In this talk, I will show that it isCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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not necessary to map the entire network to make these predictions. By using a non-parametric estimator
based on local graph features, it is possible to accurately estimate the likelihood and size of an outbreak
and the time evolution of epidemics using only a few samples from the population. This result applies toa large class of networks and can be extended to general random processes on graphs.
Remiche Marie-Ange: Sojourn time distribution in a fluid queue with server vacations
Deiana Eleonora1 , Latouche Guy2 , Remiche Marie-Ange1
1 - Université de Namur, Faculté d’Informatique (Belgium)
2 - Universite Libre de Bruxelles (Belgium)

Abstract: We use a Markov driven fluid queue with infinite buffer to model a system with server vacations.
In our setting, servers enter into vacation as soon as two conditions are fulfilled. Not only must the buffer
be empty but also the input rate of arriving fluid has to be null. The stationary distribution of the buffer
content is obtained using a regenerative approach. We determine the sojourn time distribution using its
Laplace-Stieltjes transform. To that end, we define a new clock that increases at the same rate as new
fluid enters the buffer. In this talk, we highlight the machinery behind the resulting equations, keeping
our argument on an intuitive level as much as possible.

3.9.5

Invited session F3-5 (Agrawal): Learning, Optimization and Applied
Probability (Room 104)

Friday 30th, 15:30 - 17:00 - chaired by Shubhada Agrawal
Sundaresan Rajesh: Learning to detect an anomalous Markov process
Sundaresan Rajesh1,2
1 - Robert Bosch Centre for Cyber-Physical Systems (Indian Institute of Science Bangalore - 560012, INDIA India)
2 - Department of Electrical Communication Engineering (Indian Institute of Science Bangalore - 560012, INDIA India)

Abstract: The talk will discuss the problem of finding an anomalous arm in a multi-armed bandit when(a) each arm is a finite-state Markov process, and (b) the arms are restless. Here, anomaly means thatthe transition probability matrix (TPM) of one of the arms (the odd arm) is different from the common
TPM of each of the non-odd arms. The TPMs are unknown to a decision entity that wishes to find theindex of the odd arm as quickly as possible, subject to an upper bound on the error probability. We
will discuss a problem instance-specific asymptotic lower bound on the expected time required to findthe odd arm index, where the asymptotics is as the error probability vanishes. Further, we will highlighta policy based on the principle of certainty equivalence that, under a continuous selection assumption
and a certain regularity assumption on the TPMs, achieves the lower bound arbitrarily closely. The
achievability analysis is based on resolving the identifiability problem in the context of a certain lifted
countable-state controlled Markov process. This is joint work with P. N. Karthik.
Juneja Sandeep: Guided top-2 approach to optimally solve the best arm identification
problem
Agrawal Shubhada1 , Juneja Sandeep2,3 , Shanmugam Karthikeyan3 , Suggala Arun3
1 - Georgia Tech (United States)
2 - Tata Institute of Fundamental Research (India)
3 - Google Research (India)

Abstract: We consider the popular best arm identification problem where given finitely many probability
distributions or arms that can be sampled from, the aim is to sequentially generate a minimum expected
number of samples and arrive at the correct arm with a high pre-specified degree of confidence 1- delta.
Elegant lower bound max-min formulations on the sample complexity exist for this BAI problem, andalgorithms that plug-in empirical data based estimates into the lower bound and track the resulting
prescriptive allocations have been shown to match the lower bound as delta reduces to zero. These are
however computationally inefficient as they rely on repeatedly solving the max-min optimization problem.
Lately, top two methods have been devised that are much simpler and exploit some properties of the
optimal lower bound solution. They rely on sequentially sampling either the empirical best arm withprobability beta or the empirical best challenger with probability 1-beta. The drawback of these methods
is that the optimal beta is not known. In this talk, we introduce the guided top 2 algorithm that samples
the empirically best versus the challenger so that the resulting sample complexity matches the lower
bound as delta reduces to zero.

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Maguluri Siva Theja: Concentration of Contractive Stochastic Approximation: Additive
and Multiplicative Noise
Chen Zaiwei1 , Zubeldia Martin2 , Maguluri Siva Theja3
1 - California Institute of Technology (United States)
2 - University of Minnesota (United States)
3 - School of Industrial and Systems Engineering [Georgia Tech] (United States)

Abstract: We study the concentration behavior of a stochastic approximation algorithm under a contractive operator with respect to an arbitrary norm. We consider two settings where the iterates arepotentially unbounded: (1) bounded multiplicative noise, and (2) additive sub-Gaussian noise. We obtain concentration bounds on the convergence errors, and show that these errors have sub-Gaussian tails
in the additive noise setting, and super-polynomial tails (faster than polynomial decay) in the multiplicative noise setting. Moreover, our bounds hold anytime in the sense that the entire sample path lies
within a tube of decaying radius with high probability. To establish these results, we develop a novel bootstrapping argument that involves bounding the moment generating function of the generalized Moreau
envelope of the error, and the construction of an exponential supermartingale to enable the use Ville’s
maximal inequality to obtain anytime bound. These results are motivated by application in Reinfrocement Learning, and can be used to obtain high probablity convergence bounds and sample complexity
for a large class of RL algorithms including TD learning and its variants, as well as Q learning.
Agrawal Shubhada: Bandits, Empirical Likelihood Confidence Intervals, and Heavy Tailed
Distributions
Agrawal Shubhada1 , Juneja Sandeep2 , Koolen Wouter3 , Glynn Peter4
1 - School of Industrial and Systems Engineering [Georgia Tech] (United States)
2 - Tata Institute of Fundamental Research (India)
3 - Centrum Wiskunde & Informatica (Netherlands)
4 - Stanford University (United States)

Abstract: Multi-armed bandit (MAB) is a popular framework for sequential decision-making in an
uncertain environment. In the classical setup of MAB, the algorithm has access to a fixed and finite setof K unknown, independent probability distributions or arms. At each time step, having observed theoutcomes of all the previous actions, the algorithm chooses one of the K arms and receives an independent
sample drawn from the underlying distribution, which may be considered a reward. The algorithm’s goal
is either to maximize the accumulated rewards or to identify the arm with the maximum mean in as few
samples as possible. These problems are well-studied in literature, and tight lower bounds and optimalalgorithms exist when the arm distributions are known to belong to simple classes of distributions suchas single-parameter exponential family or distributions that have bounded support, etc. However, inpractice, the distributions may not satisfy these assumptions and may even be heavy-tailed. In this talk,we will look at techniques and algorithms for optimally solving these problems with minimal assumptions
on the arm distributions. A key component of designing an optimal algorithm for MAB is constructing
tight, anytime valid confidence intervals for mean. We will look at new concentration inequalities forheavy-tailed distributions, which may be of independent interest.

3.9.6

Invited session F3-6 (Chen): Learning and queueing theory (Room105)

Friday 30th, 15:30 - 17:00 - chaired by Xinyun Chen
Hong Guiyu: An online learning method for the staffing problem of Hawkes queues
Hong Guiyu1 , Chen Xinyun1
1 - The Chinese University of Hong Kong, Shenzhen (China)

Abstract: We investigate the capacity sizing problem for a single-sever queue with Hawkes arrivals andgeneral service time distributions. Due to the complexity of system dynamics, there is no analytic solution
to the problem. We develop an online learning algorithm to numerically compute the optimal service
capacity with theoretic guarantee. In particular, we prove that with properly chosen hyper-parameters,
our algorithm obtains regret of logarithmic order. To obtain the regret bound, we establish geometricergodicity for single-server queues with Hawkes input using coupling technique, which we believe isof independent interest. In the end, we apply the online learning algorithm to solve capacity sizing
problem for a variety of single-server queues with Hawkes input. The numerical results indicate a sharp
difference between GI/GI/1 and Hawkes/GI/1 queues, especially in some asymptotic regime, indicating
the importance of taking into account autocorrelation in arrival processes in queueing controls.

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Si Nian: Drift control of high-dimensional RBM: A computational method based on neuralnetworks
Si Nian1 , Ata Baris1 , Harrison Michael2
1 - The University of Chicago Booth School of Business (United States)
2 - Stanford Graduate School of Business (United States)

Abstract: We consider a stochastic control problem whose state space is the d-dimensional non-negative
orthant. The controlled process Z evolves as a Brownian motion whose covariance matrix is exogenously
specified, as are its directions of reflection from the orthant’s boundary surfaces. A controlling agent
chooses a drift vector q(t) at each time based on the history of Z, and the cost rate at time depends on
both Z(t) and q(t). In our initial problem formulation, the objective is to minimize expected discounted
cost over an infinite planning horizon, but we also treat the corresponding ergodic control problem.
Extending earlier work by Han et al. (Proceedings of the National Academy of Sciences, 2018, 8505-8510),
we develop and illustrate a simulation-based computational method that relies heavily on deep neuralnetwork technology. Our method is accurate to within a fraction of one percent, and is computationallyfeasible in dimensions up to at least d = 20. (Based on joint work with Baris Ata and Mike Harrision.)Zhang Hailun: The Generalized c/µ Rule for Queues with Heterogeneous Server Pools
Long Zhenghua1 , Zhang Hailun2 , Zhang Jiheng3 , Zhang Zhe George4
1 - Nanjing University (China)
2 - The Chinese University of Hong Kong, Shenzhen (China)
3 - Hong Kong University of Science and Technology (Hong Kong SAR China)
4 - Western Washington University (United States)

Abstract: We study the optimal control of a queueing model with a single customer class and heterogeneous server pools under two kinds of objectives. The first is to strike a balance between the holding
cost of the queue and the operating costs of the server pools. We introduce a target-allocation policy,
which assigns higher priority to the queue or pools without enough customers, for general cost functions.
Although we can prove its asymptotic optimality implementation requires solving a nonlinear optimization problem. When the cost functions are convex, we propose a dynamic priority policy referred to as
the Gc/µ rule, which is much easier to implement. When the cost functions are concave, it turns outthat a fixed priority policy is optimal. The second objective is to minimize the operating cost of theserver pools while satisfying a service-level target for customers waiting in the queue. We develop hybrid
routing policies, combining a threshold policy for the queue and the aforementioned policies for the server
pools, for different types of operating cost functions. Moreover, the hybrid routing policies coincide withseveral classical policies in the literature in special cases. Extensive simulation experiments demonstrate
the efficacy of our proposed policies.
Cheung Wang Chi: Non-Stationary Bandits with Knapsack Problems with Advice
Lyu Lixing1 , Cheung Wang Chi1
1 - National University of Singapore (Singapore)

Abstract: We consider a non-stationary Bandits with Knapsack problem. The outcome distribution
at each time is scaled by a non-stationary quantity that signifies changing demand volumes. Instead ofstudying settings with limited non-stationarity, we investigate how online predictions on the total demand
volume Q allows us to improve our performance guarantees. We show that, without any prediction, any
online algorithm incurs a linear-in-T regret. In contrast, with online predictions on Q, we propose an
online algorithm that judiciously incorporates the predictions, and achieve regret bounds that depends on
the accuracy of the predictions. These bounds are shown to be tight in settings when prediction accuracy
improves across time. Our theoretical results are corroborated by our numerical findings.

3.9.7

Invited session F3-7 (Shneer & Zocca): Mathematics of energy (Room106)

Friday 30th, 15:30 - 17:00 - chaired by Seva Shneer & Alessandro Zocca
Christianen Mark: Comparison of stability regions for a line distribution network withstochastic load demands
Christianen Mark1 , Cruise James2 , Janssen Guido1 , Shneer Seva2 , Vlasiou Maria1,3 , Zwart Bert1,4
1 - Eindhoven University of Technology (Netherlands)

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2 - Heriot-Watt University [Edinburgh] (United Kingdom)
3 - University of Twente (Netherlands)
4 - Centrum voor Wiskunde en Informatica (Netherlands)

Abstract: We compare stability regions for different power flow models in the process of charging electricvehicles (EVs) by considering their random arrivals, their stochastic demand for energy at charging
stations, and the characteristics of the electricity distribution network. We assume the distribution
network is a line with charging stations located on it. We consider the Distflow and the LinearizedDistflow models and we assume that EVs have an exponential charging requirement, that voltage dropson the distribution network stay under control and that the number of charging stations N goes to infinity.
We investigate the stability of utility-optimizing power allocations in large distribution networks for both
power flow models by controlling the arrival rate of EVs to charging stations. For both power flow models,
we show that, to obtain stability, the maximum feasible arrival rate, i.e. stability region of vehicles isdecaying as 1/N 2 , and the difference between those arrival rates is up to constants, which we compare
explicitly.
Moriarty John: The structure of optimal stopping problems under risk
Moriarty John1 , Kosmala Tomasz1
1 - Queen Mary University of London (United Kingdom)

Abstract: We present recent work on optimally stopping a diffusion process under a risk mapping.
The problem is solved by extending an approach which was originally developed by S. Christensen to
deal with bounded ambiguity over the drift term. Our method reveals the general structure of optimal
stopping problems under a broad class of Markovian risk mappings, and we discuss motivation fromenergy problems and potential applications.
Van Der Sar Erica: Optimizing Power Grid Control with Reinforcement Learning
Van Der Sar Erica1 , Bhulai Sandjai1 , Zocca Alessandro1
1 - Vrije Universiteit Amsterdam [Amsterdam] (Netherlands)

Abstract: In the past few decades, power transmission networks have changed tremendously, mostly due
to a dramatic increase in energy demand and ambitious sustainability goals. In particular, the increasing
penetration of renewable energy sources, such as wind and solar, introduces a substantial amount of
stochasticity in these already complex networks. As a result, it is becoming more and more difficult for
network operators to control power grids and keep them safe and reliable at all times. The only way toachieve this is by using the network flexibility to its fullest extent and thus being able to identify in realtime optimal control actions in a combinatorially very large action space. Multiple reinforcement learning
frameworks have been proposed in recent years to assist network operators in the decision-making forreal-time operations. In such settings, the RL agent learns how to optimally control the power network
using various actions that can change the network topology and thus, indirectly, the power flows. In thistalk, we illustrate how existing RL approaches can be improved using Graph Neural Networks (GNN)
and additional information, such as renewable and load forecasts.
Zocca Alessandro: Generating synthetic power grids using Exponential Random Graphs
Zocca Alessandro1 , Giacomarra Francesco2 , Bet Gianmarco3
1 - Vrije Universiteit, Amsterdam (Netherlands)
2 - University of Trieste (Italy)
3 - University of Florence (Italy)

Abstract: Power grids are fundamental infrastructures of our modern societies and economies. However,
only limited information on transmission power grids is released by network operators due to security concerns. Thus, there is a substantial lack of high-quality real data. To address this problem, in the last two
decades, synthetic grid generation approaches have been extensively investigated by the research community. The main challenge has been developing models flexible enough to replicate the very heterogeneous
nature and peculiar properties of real power grids.
In this talk, I will describe a novel method for the generation of synthetic transmission power grids
using Exponential Random Graph (ERG) models. From a modeling perspective, we identify edge countand k-triangles as the crucial topological characteristics for synthetic power grid generation. From a technical perspective, we develop a rigorous methodology to estimate the parameters of an ERG constrained
to the space of connected graphs. The proposed ERG model to generate the topological structure of
power grids is flexible, easy to implement, and successfully captures the desired topological properties ofCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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power grids.

3.9.8

Invited session F3-8 (Varma & Zubeldia): Stochastic Matching Networks and their Applications (Room 202)

Friday 30th, 15:30 - 17:00 - chaired by Sushil Varma & Martin Zubeldia
Hillas Lisa: Designing Service Menus for Bipartite Qeueing Systems
Caldentey Rene1 , Gupta Varun1 , Hillas Lisa1
1 - Booth School of Business [Chicago] (United States)

Abstract: We consider a multi-class multi-server queueing system, in which customers of different types
have heterogeneous preferences over the many servers available. The goal of the service provider is todesign a menu of service classes that balances two competing objectives: (1) maximize customers’ average
matching reward and (2) minimize customers’ average waiting time. A service class corresponds to a single
queue served by a subset of servers under a FCFS-ALIS service discipline. Customers act as rational
self-interested utility maximizing agents when choosing which service class to join. In particular, they join
the class that maximizes their expected ex-ante net utility, which is given by the difference between theserver-dependent service reward they receive minus a disutility based on the mean steady-state waitingtime of the service class they join. We study the problem under (conventional) heavy traffic conditions,that is, in the limit as the traffic intensity of the system approaches one from below. For an arbitrary
number of servers, we prove that if the service provider only cares about minimizing average delay ormaximizing total matching reward then very simple menus are optimal. We also provide Mixed Integer
Linear Programming (MILP) formulations for optimizing the delay-reward trade-off within fairly richand practically relevant families of menus.
Wang Qiong: A Stochastic Tracking Model: Asymptotic Analysis and Applications
Wang Qiong1 , Martin Reiman2 , Haohua Wan3
1 - Department of ISE, University of Illinois at Urbana-Champaign (United States)
2 -Columbia University (United States)
3 - University of Illinois at Urbana Champaign (United States)

Abstract: In inventory systems, control of some key processes, such as an inventory position or a backlog
level, can only be executed from one direction. For instance, an inventory position can be raised instantly
to a target level for minimizing a cost objective according to the current state. However, when thechange of the state requires the position to be reduced to a lower target, no action can be taken except
to wait for some external events, such as a new demand arrival, to bring about the reduction. The
situation motivates us to develop and analyze a generic stochastic tracking model that is defined by three
processes: a compound Poisson demand process with appropriate moment conditions on the jump size;a target process that evolves over time; and a tracking process that involves negative jumps trigged bydemand arrivals and subjects to controls that can raise its level to meet a higher target. We consider
an asymptotic regime constructed by scaling up the demand rate. We show that if the target process is
Lipschitz continuous in increments of the demand process over some periods of fixed lengths, then the
expected difference between the target and tracking processes converges uniformly to zero on the diffusion
scale over an infinite time horizon. We discuss implications of this result for inventory management, forinstance, the assemble-to-order systems, and other possible applications.
Yang Xiaotang: Human in The Loop Automation: Ride-Hailing with Remote (Tele-)
Drivers
Benjaafar Saif1 , Wang Zicheng1 , Yang Xiaotang1
1 - University of Minnesota [Twin Cities] (United States)

Abstract: Tele-driving refers to a novel concept where drivers can remotely operate vehicles (without
being physically in the vehicle). By putting the human back "in the loop," tele-driving has emerged
recently as a more viable alternative to fully automated vehicles, with ride-hailing (and other on-demandtransportation-enabled services) being an important application. Because remote drivers can be operated
as a shared resource (any driver can be assigned to any customer regardless of trip origin or destination),tele-driving has the potential to reduce the severity of the spatial mismatch between vehicle supply andcustomer demand that is often experienced in these services and the number of drivers needed. In thispaper, we quantify the potential gains that could be realized by switching from in-vehicle drivers to remotedrivers. We compare a traditional ride-hailing system with one with tele-drivers in three regimes definedCHAPTER 3. ABSTRACTS OF INVITED TALKS AND CONTRIBUTED TALKS

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by vehicle capacity. We find that: (1) if customers are impatient, a system with appropriately selected
driver capacity may significantly improve the service level (the fraction of demand that is served), or
significantly reduce the number of drivers while maintaining a similar service level; and (2) if customers
are patient, a system with remote drivers may stabilize an otherwise unstable system with in-vehicle
drivers or significantly reduce the number of drivers while maintaining a similar service level (as measured
by the expected delay experienced by customers).
Varma Sushil: Electric Vehicle Fleet and Charging Infrastructure Planning
Varma Sushil Mahavir1 , Castro Francisco2 , Maguluri Siva Theja1
1 - Georgia Institute of Technology [Atlanta] (United States)
2 - UCLA Anderson School of Management (United States)

Abstract: We analyze an optimal electric vehicle (EV) fleet and charging infrastructure capacity planningproblem in a spatial setting. As customer requests arrive at rate λ, the system operator must determine
the minimum number of vehicles and chargers along with a matching and charging policy that maximizes
the service level. We provide a sharp characterization of the fleet size and the charging infrastructure
requirements as the demand grows. While a system in which charging times are negligible needs extraθ(λ2/3 ) vehicles on top of the nominal capacity, we show that an EV system has a fundamentally different
scaling. Due to charging times, the nominal capacity of the system is increased, but this extra capacity
allows for an optimal EV dispatching policy to result in a fleet requirement of only θ(λν ) for ν ∈ (1/2, 2/3],
depending on the number of charging stations and the size of the EV battery packs. We propose thePower-of-d dispatching policy, which achieves this performance by selecting the d closest vehicles to atrip request and choosing the one with the highest battery level, thus optimizing the trade-off between
the pickup distance and balancing the state of charge across the fleet. Our study provides valuable
guidelines for determining the optimal fleet and charging infrastructure capacity for an EV-based ondemand transportation system.

3.9.9

Invited session F3-9 (Zähle): Stability and statistical inference ofstochastic optimization problems in quantitative risk management(Room 203)

Friday 30th, 15:30 - 17:00 - chaired by Henryk Zähle
Krätschmer Volker: Nonasymptotic error rates of the sample average approximation method
to solve risk averse stochastic progams
Krätschmer Volker1
1 - University Duisburg-Essen (Germany)

Abstract: We study statistical properties of the optimal value of the Sample Average Approximation.
The focus is on rates dependent on the sample sizes for the tail function of the absolute error inducedby the Sample Average Approximation. They allow to conclude immediately convergence rates for the
optimal value of the Sample Average Approximation. As a crucial point the investigations are based ona new type of conditions from the theory of empirical processes which do not rely on pathwise analyticalproperties of the goal functions. In particular, continuity in the parameter is not imposed in advanceas usual in the literature on the Sample Average Approximation method. The main results are shownfor classical risk neutral stochastic programs, but we also demonstrate how to apply them to the sample
average approximation of risk averse stochastic programs. In this respect we consider stochastic programs
expressed in terms of mean upper semideviations and divergence risk measures.
Wiesel Johannes: Sensitivity analysis of Wasserstein distributionally robust optimization
problems
Bartl Daniel1 , Drapeau Samuel2 , Obloj Jan3 , Wiesel Johannes4
1 - University of Vienna (Austria)
2 - Shanghai Jiao Tong University (China)
3 - Oxford University (United Kingdom)
4 - Columbia University (United States)

Abstract: In this talk I consider sensitivity of a generic stochastic optimization problem to model uncertainty, where I take a non-parametric approach and capture model uncertainty using Wasserstein balls
around the postulated model. I provide explicit formulae for the first order correction to both the valuefunction and the optimizer and further extend our results to optimization under linear constraints. Then I

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present applications to statistics, machine learning, mathematical finance and uncertainty quantification.
In particular, I prove that LASSO leads to parameter shrinkage, propose measures to quantify robustnessof neural networks to adversarial examples and compute sensitivities of optimised certainty equivalents
in finance. I also propose extensions of this framework to a multiperiod setting.
Xu Huifu: Robust Spectral Risk Optimization When Information on Risk Spectrum Is
Incomplete
Wang Wei1 , Xu Huifu1
1 - The Chinese University of Hong Kong (Hong Kong SAR China)

Abstract: A spectral risk measure (SRM) is a weighted average of value at risk where theweighting
function (also known as risk spectrum or distortion function) characterizes a decision maker’s risk attitude.
In this work, we consider the case where the decision maker’s risk spectrum isambiguous and introduce arobust SRM model based on the worst risk spectrum from a ball of riskspectra centered at a nominal risk
spectrum. When the ball consists of step-like risk spectra, we showthat the robust SRM can be computedby solving a linear programming problem. For the general case, we propose a step-like approximation
scheme and derive an error bound for the approximation.As an application, we apply the proposed robust
SRM to one-stage stochastic optimization with theobjective of minimizing the robust SRM and propose
an alternating iterative algorithm for solvingthe resulting minimax optimization problem. Moreover, toexamine stability of the robust spectralrisk optimization model with respect to perturbation of observed
data from the underlying exogenous uncertainty in data-driven environments, we investigate statisticalrobustness of the model and derive sufficient conditions for the required stability.
Zähle Henryk: A concept of copula robustness and its applications
Zähle Henryk1
1 - Saarland University (Germany)

Abstract: In financial and actuarial applications, marginal risks and their dependence structure areoften modelled separately. While it is sometimes reasonable to assume that the marginal distributions
are "known", it is usually quite involved to obtain information on the copula. Therefore copula models
used in practice are quite often only rough guesses. For many purposes, it is thus relevant to knowwhether certain characteristics derived from d-variate risks are robust with respect to (at least small)deviations in the copula. In this talk, a general concept of copula robustness is introduced and criteria
for copula robustness are presented and illustrated by examples.

3.9.10Contributed session F3-10 (Minabutdinov): Contagion and Systemicrisk (Room 204)

Friday 30th, 15:30 - 17:00 - chaired by Aleksei Minabutdinov
Hakim Arief: Conditional quantile and expectile-based systemic risk measures and systemic
networks for dirty and clean energy markets
Hakim Arief1 , Syuhada Khreshna1
1 - Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung (Indonesia)

Abstract: Conditional quantiles are crucial for many statistical problems, including systemic risk quantification. Specifically, we can determine conditional quantile-based Value-at-Risk (hereinafter, CoQVaR),
which measures a financial entity’s risk conditional on another entity’s risk and thus quantifies the systemic impact of the latter on the former. However, CoQVaR depends only on the probability of risk
occurrence and exhibits incoherence. This leads us to propose conditional expectile-based Value-at-Risk(henceforth, CoEVaR), which accounts for risk magnitude and satisfies risk measure coherence. In thisstudy, we aim to formulate CoQVaR and CoEVaR. Since their bases (i.e., conditional quantiles and expectiles) are determined by optimizing expected conditional loss functions, we adopt an efficient method
for the computation of conditional expectations. We compare the performances of the resulting CoQVaR
and CoEVaR forecasts by evaluating the estimates of their respective expected conditional loss functions.
As an application, we perform an empirical study using the daily return series of dirty energy commodities and clean energy indices. In addition, we compute the so-called Delta-CoQVaR and Delta-CoEVaR
to quantify the systemic risk contribution of one energy market to the other. Using these measures, wethen construct systemic networks to describe the mechanism of systemic risk transmission across theseenergy markets. This approach helps us investigate the systemic relevance and fragility of each energy
market and maintain the stability of global energy markets.

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Lagos Guido: An asymptotic systemic failure time result for mixed coherent systems
Lagos Guido1 , Barrera Javiera1,2 , Romero Pablo3,4 , Valencia Juan5
1 - Universidad Adolfo Ibáñez [Viña del Mar] (Chile)
2 - Northwestern University [Evanston] (United States)
3 - Universidad de la República [Montevideo] (Uruguay)
4 - Universidad de Buenos Aires [Buenos Aires] (Argentina)
5 - Universidad de Santiago de Chile [Santiago] (Chile)

Abstract: In this work we show a limit result for the probability of a system being in an operational(working) state as the size of the system grows to infinity. More specifically, we consider a sequence of
mixed coherent systems whose components are homogeneous and non-repairable, with failure times thatallow simultaneous failures of the components. We show that under dominated convergence conditions
the system failure time converges in distribution to a first-passage time of a Levy subordinator process.
To illustrate, we give a parametric family of systems where the time of systemic failure converges to an
exponential distribution. To the best of our knowledge, this is the first result to tackle the asymptotic
behavior of the system failure time as the number of components of the system grows.
Amanihamedani Alireza: Spatial Matching under Resource Competition
Amanihamedani Alireza1 , Freund Daniel2 , Aouad Ali1
1 - London Business School (United Kingdom)
2 - MIT (United States)

Abstract: Motivated by ride-hailing marketplaces, we present a model of two-sided matching platforms
in a duopoly market with spatial frictions. Both platforms face disjoint streams of rider requests, but theyshare the same resources on the supply side, i.e., drivers multi-home. Due to spatial frictions, supply
efficiency requires an admission control to maintain a buffer of available drivers to balance matching
distances with other supply costs. We consider two types of platform admissions controls, "protection"and "distance thresholds", under a large-market stochastic model. For both controls, we find that a
duopoly equilibrium entails at least one platform "undercutting", by accepting all requests, to gain
market share. When both platforms undercut, the supply buffer is not maintained and inefficiencies
ensue. In contrast, when just one platform undercuts, then the duopoly is asymptotically as efficient as amonopolist. For the protection thresholds, we provide a classifier of market characteristics that delineates
the two regimes and allows us to characterize the prices of anarchy and stability in these settings.
Minabutdinov Aleksei: Misfortunes Never Come Singly: Managing the Risk of Chain Disasters
Minabutdinov Aleksei1 , Bretschger Lucas, Renoir Clément, Brausmann Alexandra
1 - Department of Management, Technology, and Economics [ETH Zürich] (Switzerland)

Abstract: The recent COVID-19 pandemic has confirmed two facts: First, a large initial disaster - thepandemic - is capable of triggering a whole chain of secondary economy-wide disasters, like a domino
effect. Second, governments around the world implemented disaster-mitigation efforts only after thegravity of the pandemic became obvious, as opposed to taking precautionary measures ahead of time. In
this paper, we question the optimality of such a "reactive" approach to disaster management by studying
disaster-prevention and growth policies in an environment where a primary shock to the economy may
provoke future calamities through contagion effects. We develop a novel dynamic stochastic framework,where disaster arrivals are modeled via the Hawkes process. We derive analytical solutions and showthat the optimal policy consists of devoting a stochastic fraction of output to disaster-mitigation. The
mitigation propensity is an increasing function of the Hawkes intensity and essentially tracks disasterarrivals. The latter implies that the policy is indeed reactive. This result is in contrast with the existing
literature, which does not take into account the possibility of contagion and therefore finds a constantmitigation propensity to be optimal.

Chapter 4

Abstracts of posters
4.1

Poster session I

4.1.1

Bharti Bharti: Spatial appointment scheduling in a random environment
Bharti Bharti1 , Mandjes Michel1 , Bekker René2
1 - Korteweg-de Vries Institute for Mathematics (Netherlands)
2 - Vrije Universiteit, Amsterdam (Netherlands)

Abstract: In this work, we investigate single server delivery problems using a stochastic approach. In
our model we distinguish two factors; namely given the number of delivery locations, we wish to decideboth on an appropriate route, as well as on appropriate delivery times. Here both the travel times
between the locations and the service times at the locations are random variables. The aim is to optimize
the objective function determined (which contains the nodes’ idle- and waiting times) by a given routeand inter-appointment times. The overall process is challenging since it is difficult to obtain a closedform expression for the objective function. In this work, therefore, we follow three approaches: (1) an
accurate (but slow) approximation approach using phase-type distributions that are mixtures of Erlang
distributions and hyperexponential distributions, (2) a simplification of the objective function based ona heavy-traffic approximations, and (3) a ‘lag-based’ approach in which some ‘memory’ is ignored so as
to avoid the multiplication of large matrices. For the routing component, which in itself is an NP-hardproblem, classical heuristic methods which destroy and repair edges have been tested and implemented.
Empirical results illustrate that the combination of these techniques lead to accurate and efficient results.

4.1.2

Brodu Virgile: An individual-based model for food webs: how can
simple biological constraints enforce precise allometric coefficients.
Billiard Sylvain1 , Brodu Virgile2 , Champagnat Nicolas2,3 , Fritsch Coralie2,3
1 - Évolution, Écologie et Paléontologie (Evo-Eco-Paleo) - UMR 8198 (France)
2 - Institut Élie Cartan de Lorraine (France)
3 - Inria Nancy - Grand Est (France)

Abstract: For a few decades, a lot of papers in the field of evolutionary ecology have stood for a very
particular relationship between body mass or length and metabolism, across several orders of magnitudeof species size. It is now well-known as an allometric relationship, or simply allometry, presented in theform B ∝ M α , where B is the metabolism, M the mass and α the so-called allometric coefficient. Even
if these allometries seem to be a key ingredient for modelling the behavior of species, their mass andecological features, it is still mainly justified by experimental approaches.
We design a simple individual-based model, structured by a trait called energy. We study species
characterized by a typical energy, consuming a resource maintained at a fixed level. The whole system
is ruled by two sorts of mechanisms: random jumps conforming to births and deaths in the population;
and a continuous and deterministic evolution of individual energies over time. Importantly, we will allow
ourselves to consider the broad range of possible characteristic energy, either really small or high, andthus consider very different species and not one in particular. It means that we design a model focusing
on interspecific allometries, rather than intraspecific dynamics with a fixed typical energy.
First, we enforce with basic probabilistic tools sharp values for the allometric coefficients involved in
our model, showing that this allometric setting is very restrictive. This idea is also supported by a more
complex property of asymptotic pseudotrajectory verified by our underlying stochastic process, leading
to precise asymptotic behaviors.
133

CHAPTER 4. ABSTRACTS OF POSTERS

4.1.3

134

Cocomello Juniper: Tractable Approximations of non-Markovian S(E)IR
dynamics on locally tree-like graphs
Cocomello Juniper1 , Ramanan Kavita2
1 - Brown University (United States)
2 - Brown University (United States)

Abstract: The Susceptible-Infected-Recovered (SIR) process is a widely used mathematical model of
infectious diseases. We develop asymptotically exact and computationally tractable approximations of
the ensemble behavior of the SIR process on a large class of sparse graphs that converge locally to a tree
(which includes uniform d-regular graphs, sparse Erdos-Rényi and the Configuration Model). The model
we consider allows for age-dependent recovery rates, and is therefore non-Markovian. We also generalize
our result to the SEIR model.

4.1.4

Deep Vikas: Optimal inference of ATE in A/B testing
Deep Vikas1 , Juneja Sandeep2 , Bassamboo Achal1
1 - Northwestern University (United States)
2 - Tata Institute of Fundamental Research (India)

Abstract: A/B Testing is a popular statistical test used in practice on various online platforms forwebsite optimization, product offerings etc. For example, if a firm wants to asses some performance
metric of the existing version of their product, named A, against a new version of product B, they
conduct A/B testing. Most importantly, firms would like to do valid statistical inference about the effectsize, i.e., average treatment effect (ATE) of a variation from the existing version of the product to a
new version of the product. In this paper, Our objective is to arrive at a confidence interval (CI) of apre-specified width ε, and with a coverage guarantee that the ATE lies in the CI with probability at least1δ for pre-specified δ ∈ (0, 1). The novelty of our analysis is the derivation of the asymptotic lower boundon the number of samples required by any algorithm to construct a CI of ε−width with the coverage
guarantee for fixed ε > 0 in the regime when δ → 0, and the construction of an algorithm that, under mild
assumptions, matches the lower bound. We utilize bandit algorithms (adaptive algorithms) for reliable
inference methods based on the resulting adaptively-collected data. Surprisingly, we get that there are
two optimal adaptive experimentation policies and there is value to use adaptive experimental policies
over randomized policies.

4.1.5

Deka Prabhanka: PageRank Nibble on the sparse directed stochastic
block model
Banerjee Sayan1 , Deka Prabhanka1 , Olvera-Cravioto Mariana1
1 - University of North Carolina, Chapel Hill (United States)

Abstract: We present results on community recovery in a sparse, directed Stochastic Block Model (dSBM)
where the identities of a positive proportion of seeds in one community is known. Our method is based on
the PageRank Nibble algorithm, which uses personalized PageRank and a simple threshold rule to label
vertices in the same community as the seeds. This method leverages information about the community
label of some vertices, a situation often encountered in practice. Moreover, since PageRank is a local
algorithm that can be computed in a distributed and asynchronous fashion, it is highly efficient even inlarge graphs. Our estimates on the probability of misclassification are derived from a tractable expression
for the limiting PageRank distribution. This distribution is in turn obtained by using a new local weaklimit for a very general class of inhomogeneous random digraphs, which allows us to characterize it via asystem of distributional fixed point equations.

4.1.6

Gupta Hritika: Expected Number of Call Abandonments in a Call
CentreGupta Hritika1,2

1 - University of Melbourne (Australia)
2 - ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA)(Australia)

Abstract: Most call centres are a mix of dedicated servers and multi-skilled servers. The dedicatedservers are trained to solve one type of customer query and the multi-skilled servers are trained to solve
more than one type of customer query. In such call centres, there are generally multiple ways of allocating

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135

an incoming call to a server. Some allocation methods might perform better than others. One of themeasures that can be used to compare the performance of these methods is the total number of calls thatwere abandoned in a given time interval when using that method to allocate the calls. Assessing callabandonments is crucial for a call centre as they reflect an unsatisfactory customer experience and lossof revenue in some cases. Our aim is to calculate the expected number of call abandonments during afuture time interval given the number of callers currently present in the system for any given allocation
method. We can then use it to find an optimal call allocation policy that minimises the expected numberof call abandonments in a call centre. We will make use of Laplace transforms in order to achieve this.
This method can also be used to calculate other performance measures related to call centres.

4.1.7

Hanukov Gabi: Postponed Complementary Services
Hanukov Gabi1
1 - Ariel University (Israel)

Abstract: Studies on service systems and queueing theory classically assume that a customer who obtains
service must be present in the system during the whole time the service is provided. However, as time
passed, many service systems realized that part of the service can be performed while the customer isabsent. The customer can use this time efficiently outside the system. Adopting this potentially favorable
approach, we study a Markovian service system in which the service can be split into two phases: an
opening service (OS), which can be provided only when the customer is present, and a complementary
service (CS), which can be carried out in the absence of the customer. To improve the system’s overall
performance, after providing the OS, the server can postpone executing the CS to time intervals in which
the system is empty of customers. The prepared OSs are stored in a designated storage facility. Oncethe system becomes empty, the server executes the CS for the stored OSs, one at a time. We consider
two unbounded dimensions system involving a multivariate probability generating function and deriveclosed-form expressions for the system’s steady-state probabilities and its various performance measures.
An economic analysis compares the described model with a corresponding limited OS capacity model.

4.1.8

Hillas Lisa: Designing Service Menus for Bipartite Qeueing Systems
Caldentey Rene1 , Gupta Varun1 , Hillas Lisa1
1 - Booth School of Business [Chicago] (United States)

Abstract: We consider a multi-class multi-server queueing system, in which customers of different types
have heterogeneous preferences over the many servers available. The goal of the service provider is todesign a menu of service classes that balances two competing objectives: (1) maximize customers’ average
matching reward and (2) minimize customers’ average waiting time. A service class corresponds to a single
queue served by a subset of servers under a FCFS-ALIS service discipline. Customers act as rational
self-interested utility maximizing agents when choosing which service class to join. In particular, they join
the class that maximizes their expected ex-ante net utility, which is given by the difference between theserver-dependent service reward they receive minus a disutility based on the mean steady-state waitingtime of the service class they join. We study the problem under (conventional) heavy traffic conditions,that is, in the limit as the traffic intensity of the system approaches one from below. For an arbitrary
number of servers, we prove that if the service provider only cares about minimizing average delay ormaximizing total matching reward then very simple menus are optimal. We also provide Mixed Integer
Linear Programming (MILP) formulations for optimizing the delay-reward trade-off within fairly richand practically relevant families of menus.

4.1.9

Huang Junfei: Capacity Allocation and Scheduling in Two-Stage Service Systems with Multi-Class Customers
Zhong Zhiheng, Cao Ping, Huang Junfei1 , Zhou Sean
1 - The Chinese University of Hong Kong (Hong Kong SAR China)

Abstract: This paper considers a tandem service system and the objective is to design a joint capacity
allocation and scheduling policy to minimize the long-run average cost. We develop an iterative procedure
to solve the fluid problem, and translate the solution into an easy-to-implement policy for the stochastic
system. For two special structures, we derive explicit index-based policies.

CHAPTER 4. ABSTRACTS OF POSTERS

4.1.10136

Johlke Anika: Control Charts for Statistical Process Control - A Literature Overview
Johlke Anika1
1 - Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany] (Germany)

Abstract: Control charts as a tool for statistical process control have been regularly researched andapplied for decades. If the underlying assumptions are guaranteed, traditional charts such as the Shewhart, EWMA or CUSUM chart are a reasonable choice for monitoring production or service processes.
However, the charts themselves have evolved in many aspects, partly due to the increasing availabilityof data or the need for more complex monitoring processes, so that this field of research has become
extremely broad and diverse. This literature review aims to categorize the different characteristics ofcontrol charts, namely general and specific types of control charts, different types of data, multivariate
and distribution-free charts, or the economic approach for designing a control chart. Especially, a categorization scheme for different performance metrics is provided. All categories are briefly summarized
and representative articles are overviewed, the most influential authors in the field are introduced, andseveral suggestions for future research are concluded. Unlike other recent literature reviews, this review
does not focus on a specific property of control charts, but rather provides a broad overview. Researchon this topic has not ceased, in fact it has increased in the past year, making control charts as important
as ever.

4.1.11

Khodadadian Sajad: Federated reinforcement learning: Linear speedup
under Markovian sampling
Khodadadian Sajad1 , Sharma Pranay, Joshi Gauri, Maguluri Siva Theja
1 - School of Industrial and Systems Engineering [Georgia Tech] (United States)

Abstract: Reinforcement learning (RL) is a sequential decision making paradigm where an agent learns
to accomplish certain tasks by interacting with the environment. It is known that RL algorithms aredata-intensive, and require a large set of data to train. One way to boost the learning of RL algorithms is
to employ multiple agents to collect data. Furthermore, in certain applications such as medical settings,
data of the local agents might be sensitive and needs to be kept private. In this talk, we consider afederated RL framework where multiple agents collaboratively learn a global model, without sharing
their sensitive individual data and policies. Although having N agents enables the sampling of N times
more data, it is not clear if it leads to proportional convergence speedup. We consider federated versionsof on-policy TD, off-policy TD and Q-learning, and establish that there is a speedup in learning that islinear in the number of agents. In particular, we show this even in the presence of Markovian noise andmultiple local updates. We do this by developing a federated stochastic approximation algorithm with
Markovian noise (FedSAM) and establishing linear speedup under a very general framework.

4.1.12

Koopmans Camiel: Optimal service control and observation restoration decisions in Markovian queues with fallible sensors.
Kanavetas Odysseas1 , Koopmans Camiel1 , Spieksma Floske1
1 - Mathematical Institute, Leiden University (Netherlands)

Abstract: Markov decision processes (MDPs) have been a source of many techniques and models frequently used nowadays. This talk aims to show how to apply techniques stemming from MDPs to
Markovian single-queue models where service rate control is performed with fallible sensors. More specifically, “fallible”means that after a sensor failure the queue cannot be observed until it is repaired. Thisdiffers from noisy partially-observable MDPs. In practice, such a model is applicable to situations where
sensors break down and their repairs have to be optimized. We search for policies that minimize costsby specifying the service rate to use and by scheduling the repair of the sensor. One of the approaches isbased on the fact that cer- tain mathematical properties (e.g., monotonicity and convexity) are preserved
under Value Iteration, which allows us to establish certain (threshold) structures in optimal policies.

4.1.13

Kraakman Yanna: Configuration models of random directed hypergraphs
Kraakman Yanna1 , Litvak Nelly1,2 , Stegehuis Clara1
1 - University of Twente (Netherlands)
2 - Eindhoven University of Technology [Eindhoven] (Netherlands)

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137

Abstract: Many real-world networks exhibit interactions beyond pairwise connections, which are called
hyperedges. What is often overlooked, is that hyperedges can also be directed. For chemical reaction
networks for example, the reaction A+B -> C+D is a hyperedge connecting two sets of interacting
species. Such networks can be represented by directed hypergraphs, which can be categorized into
different classes. The central question we investigate is: how can we generate directed hypergraphs insuch a way that they are sampled uniformly? We therefore introduce configuration models that cangenerate random directed hypergraphs for the different classes, and investigate when they generate these
uniformly. Subsequently, we propose a Markov chain Monte Carlo method to sample them. Interestingly,
we show that the proposed Markov chain Monte Carlo method generates uniformly distributed samples
from the configuration models of certain classes of directed hypergraphs, and not from others.

4.1.14

Lee Junghyun: Communication-Efficient Collaborative Heterogeneous
Bandits in Networks
Lee Junghyun1 , Schmid Laura1 , Yun Se-Young1
1 - Korea Advanced Institute of Science and Technology (South Korea)

Abstract: The multi-agent multi-armed bandit problem has been studied extensively due to its ubiquity inmany real-life applications, such as online recommendation systems and wireless networking. We consider
the setting where agents should minimize their group regret while collaborating over a given graph via
some communication protocol and where each agent is given a different set of arms. Previous literatureon this problem only considered one of the two desired features: agents communicate over a generalgraph while having the same arm set, or the graph is fully connected while each agent has a different
arm set. We first introduce a more general problem setting that encompasses all the desired features.
For this novel setting, we first provide theoretical analyses of the standard flooding protocol combinedwith the UCB algorithm. Then, to mitigate the issue of huge communication costs incurred by flooding,we propose a new protocol called Flooding with Absorption (FWA). We provide theoretical analyses andinformal intuitions on the advantages of using FWA over flooding. Lastly, we verify empirically that using
FWA leads to significantly lower communication costs despite minimal regret performance loss compared
to flooding.

4.1.15

Nagy Oliver: Mixing of fast random walks on dynamic random permutations
Nagy Oliver1
1 - Universiteit Leiden (Netherlands)

Abstract: We analyse the mixing profile of a random walk on a dynamic random permutation, focusing
on the regime where the walk evolves much faster than the permutation. Two types of dynamics generated
by random transpositions are considered: one allows for coagulation of permutation cycles only, the otherallows for both coagulation and fragmentation. We show that for both types, in the limit as the lengthof the permutation tends to infinity and after an appropriate scaling of time, the total variation distance
between the current distribution and the uniform distribution drops down in a single jump. This jump
is similar to a one-sided cut-off, occurs after a random time, and goes from the value 1 to a value thatis a strictly decreasing and deterministic function of the time of the jump. After the jump, the total
variation distance follows the aforementioned function down to 0. Joint project with Luca Avena (LeidenU.), Remco van der Hofstad (TU Eindhoven) and Frank den Hollander (Leiden U.)

4.1.16

Nieto Benoît: Estimation of threshold diffusions
Mazzonetto Sara1 , Nieto Benoît2
1 - Institut Élie Cartan de Lorraine (France)
2 - Institut Camille Jordan [Villeurbanne] (France)

Abstract: We refer by threshold diffusions to a class of continuous-time Markov process which admitsa change of dynamics on a fixed level. We study the (quasi)-maximum likelihood estimation of the drift
parameters, for continuous and discrete time observations. Two consistent estimators for the volatility
parameters are also proposed. We discuss about the consistency and the speed convergence of theseestimators in long time and high frequency for the ergodic case. Based of these results, we propose anapplication to the Threshold Cox-Ingersoll-Ross (CIR) model.

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4.1.17

138

Qu Yanlin: Contractive Drift Conditions for Rates of Convergence ofMarkov Chains in Wasserstein Distance
Qu Yanlin1 , Blanchet Jose1 , Glynn Peter1
1 - Stanford University (United States)

Abstract: We introduce a condition called "contractive drift" to bound the convergence of Markov
chains in Wasserstein distance. Under this condition, we derive computable convergence bounds withdifferent rates, from polynomial to exponential. These bounds are computable in the sense that they only
contain fully explicit constants and one-step transition expectations. To enhance practical application,
we develop various techniques to verify the contractive drift condition so that computable convergencebounds can be conveniently obtained for queueing models as well as optimization algorithms. Moreover,
our bounds have the ability to capture optimal rates of convergence in terms of model parameters suchas the traffic intensity in queueing models and the step size in optimization algorithms.

4.1.18

Reid Mirabel: Convergence of the k-cap Process on Graphs with
Weight Reciprocity
Kailasa Sameer1 , Moondra Jai2 , Reid Mirabel2
1 - University of Michigan (United States)
2 - Georgia Institute of Technology (United States)

Abstract: Artificial neural networks utilizing a k-winners-take-all activation mechanism, or k-cap, havebeen proposed as a model of excitatory-inhibitory biological networks dominated by strong lateral inhibition. In conjunction with Hebbian plasticity of weights, such networks form the backbone of the assembly
calculus, an emerging mathematically tractable theory for formation and manipulation of memories.
In this work, we study the dynamics of random recurrent neural networks employing the k-cap operation, with underlying network connectivity drawn from a chosen graph model. The process leads toa sequence of k-subsets of vertices, called ‘active sets’; at each discrete time step, the active set consistsof the k vertices with the highest weighted degree sum from the previous active set. We establish theconvergence of this process on unweighted undirected graphs and some weighted random graphs. Complementing recent work which analyzes the same process on soft geometric random graphs, our results
do not require plasticity of weights, indicating two distinct mechanisms by which activity in biologicalneural networks may become localized.

4.1.19Schmocker Philipp: Universal Approximation Property of Random
Neural Networks
Neufeld Ariel1 , Schmocker Philipp1
1 - Nanyang Technological University (Singapore)

Abstract: In this talk, we study single-hidden-layer feedforward neural networks with randomly initialized
weights and biases, which are inspired by the works on extreme learning machines, random feature models,
and reservoir computing. After the random initialization, only the linear readout needs to be trained,
which can be performed, e.g., by a linear regression. Despite the popularity of this approach in empiricaltasks, only little is known about the approximation properties. By considering these “random neuralnetworks” as Banach space-valued random variables, we prove some universal approximation theorems
within Bochner spaces. Hereby, the Banach space can be a more general function spaces such as Lp -spaces
or Sobolev spaces, where the latter includes the approximation of the derivatives. In addition, we provide
some approximation rates for random neural networks, which shows in particular that they overcome thecurse of dimensionality on a dimension-scaled neighborhood of the training data. This talk is based on
joint work with Ariel Neufeld.

4.1.20Vande Velde Sylvie: Branching Processes and Approximate Bayesian
Computation Applied to the Initiation of Chronic Myeloid Leukaemia
Vande Velde Sylvie1 , Engelbeen Céline2 , Hautphenne Sophie3
1 - Université libre de Bruxelles (Belgium)
2 - Brussels Management School [Bruxelles] (Belgium)
3 - University of Melbourne (Australia)

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Abstract: Chronic Myeloid Leukemia (CML) is a type of cancer that occurs when there is an exchange ofgenetic material between chromosomes 9 and 22 in certain blood-forming cells of the bone marrow. This
results in the growth and division of leukemia cells that accumulate in the bone marrow and differentiate
into more mature cells, which then spread throughout the blood. Previous studies have demonstrated
that the formation of normal blood cells can be modelled using a 32-compartment model, where eachcompartment represents a stage of cell differentiation from stem cells to mature blood cells [1, 2]. Building upon this assumption, we use a special class of branching processes known as Markovian Binary
Tree (MBT) [3] to model the development of the number of normal and leukemia cells in the 32 compartments. Our project aims to determine the stage of cell differentiation where the first chromosomal
exchange occurred with the highest probability. To achieve this goal, we use real patient data [5] andapply the Approximate Bayesian Computation (ABC) method [4]. This method provides a probability
distribution over the compartment where the initial leukemia cell appeared, which enables us to test
the correlation between the origin compartment and the treatment response for groups of patients withsimilar characteristics. References [1] D. Dingli, A. Traulsen, and J. M. Pacheco, Compartmental architecture and dynamics of hematopoiesis, PLOS ONE, vol. 2, pp. 14, 2007. [2] B. Werner, D. Dingli, T.
Lenaerts, J. M. Pacheco, and A. Traulsen, Dynamics of mutant cells in hierarchical organized tissues,
PLOS Computational Biology, vol. 7, pp. 19, 2011. [3] S. Hautphenne, M. Massaro, and K. Turner,
Fitting Markovian binary trees using global and individual demographic data, Theoretical Population
Biology, vol. 128, pp.39-50, 2019. [4] M. SunnËŽaker, A.G. Busetto, E. Numminen, J. Corander, M. Foll,
C. Dessimoz, Approximate Bayesian Computation, PLoS Comput Biol, vol. 9(1), 2013. [5] I. S. Pagani
et al, BCR-ABL1 genomic DNA PCR response kinetics during first-line imatinib treatment of chronic
myeloid leukemia, Haematologica, vol. 103(12), pp. 2026-2032, 2018.

4.1.21

Van Der Sar Erica: Optimizing Power Grid Control with Reinforcement Learning
Van Der Sar Erica1 , Bhulai Sandjai1 , Zocca Alessandro1
1 - Vrije Universiteit Amsterdam [Amsterdam] (Netherlands)

Abstract: In the past few decades, power transmission networks have changed tremendously, mostly due
to a dramatic increase in energy demand and ambitious sustainability goals. In particular, the increasing
penetration of renewable energy sources, such as wind and solar, introduces a substantial amount of
stochasticity in these already complex networks. As a result, it is becoming more and more difficult for
network operators to control power grids and keep them safe and reliable at all times. The only way toachieve this is by using the network flexibility to its fullest extent and thus being able to identify in realtime optimal control actions in a combinatorially very large action space. Multiple reinforcement learning
frameworks have been proposed in recent years to assist network operators in the decision-making forreal-time operations. In such settings, the RL agent learns how to optimally control the power network
using various actions that can change the network topology and thus, indirectly, the power flows. In thistalk, we illustrate how existing RL approaches can be improved using Graph Neural Networks (GNN)
and additional information, such as renewable and load forecasts.

4.1.22

Wang Xingyu: Eliminating Sharp Minima from SGD with TruncatedHeavy-tailed Noise
Wang Xingyu1 , Oh Sewoong2 , Rhee Chang-Han1
1 - Northwestern University (United States)
2 - University of Washington (United States)

Abstract: The empirical success of deep learning is often attributed to the stochastic gradient descent(SGD) algorithm’s mysterious ability to avoid sharp local minima in the loss landscape, as sharp minima
are known to lead to poor generalization. Recently, evidence of heavy-tailed gradient noise was reported
in many deep learning tasks, and Simsekli et al. (2019) argued that SGDs are capable of escaping sharp
local minima under the presence of such heavy-tailed gradient noise. This suggests a new promising
perspective to the mystery, but when heavy-tailed gradient noises are present, the pure form of SGD
often fails to train DNNs altogether, and one needs to deploy a variant of SGD, where gradients aretruncated above a fixed threshold. In this talk, we analyze the global dynamics of such a variation (often
called gradient clipping) and show that they are fundamentally different from those of the pure form ofSGD. In fact, this variant attains a strong notion of avoiding sharp minima: it can effectively eliminate
sharp local minima entirely from its training trajectory. We prove this by rigorously establishing an
Eyring-Kramers-type first exit time formula and metastability analysis for heavy-tailed SGDs: We reveal
the large deviations in heavy-tailed SGDs and obtain a sharp characterization of the relationship between
the first exit time from a local basin and the ratio between the truncation threshold and the width of

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the basin. Moreover, when the objective function satisfies appropriate structural conditions, we provethat the heavy-tailed truncated SGD converges to a continuous-time Markov chain that never visits any
sharp minima as the step size decreases. Real data experiments on deep learning confirm our theoreticalprediction that heavy-tailed SGDs with gradient clipping find flatter local minima and achieve better
generalization.

4.1.23

Zhang Dongni: Epidemic models with manual and digital contact
tracingZhang Dongni1 , Britton Tom1
1 - Stockholm University (Sweden)

Abstract: We start with a Markovian SIR epidemic model in a homogeneous mixing community with aconstant diagnosis rate. First, we introduce manual contact tracing by assuming that once an infective
is diagnosed (tested positive and isolated), each of her/his contacts is immediately traced and tested
independently with some fixed probability. Using large population approximations, we analyzed the earlystage of the outbreak when the process of "to-be-traced components" behaves like a branching process.
The component and individual reproduction numbers are derived. Then we focus on the more recent
digital contact tracing via a tracing app (only app-users can trigger and be traced by digital tracing).
And we assume that there is a fixed app-using fraction and that digital tracing occurs instantaneously
and recursively. The model with digital tracing is analysed by a two-type branching process relying ona large community, where one type is "app-using components," and another is non-app-users. Further,
we investigate the combined preventive effect of manual and digital tracing. This combined model isanalysed by a different two-type branching process with both types being the "to-be-traced components"
but starting with different "roots". The corresponding reproduction numbers are derived. We conclude
that to control the epidemic, we always need to have a large fraction of app-users compared to the fractionof individuals being successfully reached by manual tracing. Another important conclusion is that the
combined effect is bigger than the product of two separate preventive effects. Finally, our ongoing work
is based on an SEIR epidemic model on a configuration model, where transmission could happen fromrandom contacts (e.g., on a bus), which are usually more easily identified by using the tracing app.
Each infective remains infectious for a constant period. Meanwhile, we assume that such an infective isdiagnosed and asked for an interview with some probability otherwise, we say that the infective recovers
naturally. Once diagnosed, each of her/his infectee neighbours (manual tracing only on the network) is
reported with some probability independently. If such reported neighbours are infectious after some delay,
they are isolated and said to be traced. If the diagnosed person is also an app-user, all of her/his appusing contacts are traced immediately (digital tracing both on the network and among global contacts).
And in this case, we assume that only the diagnosed individuals can trigger contact tracing.

4.2

Poster session II

4.2.1

Abdelhakmi Anas: A multi-period Black-Litterman model with desynchronized expert views
E.b. Lim Andrew1 , Abdelhakmi Anas2

1 - Department of Analytics and Operations, Department of Finance, and Institute of Operations Research and Analytics,
National University of Singapore (Singapore)2 - Institute for Operations Research and Analytics, National University of Singapore (Singapore)

Abstract: The Black-Litterman model is a framework for incorporating forward-looking expert views in a
portfolio optimization problem. Existing work focuses almost exclusively on single-period problems where
the horizon of expert forecasts matches that of the investor. We consider a multi-period generalization of
the Black-Litterman model where the investor trades dynamically, and the horizon of expert views may
differ from that of the investor. By exploiting an underlying graphical structure relating the asset priceprocess and the forward-looking expert views, we derive the conditional distribution of asset returns when
the price process is assumed to be geometric Brownian motion. The new price process is an affine factor
model where an adjustment of the unconditional log-price process is a factor, and is closely related tothe smoothed estimates of future asset prices. We derive an explicit expression for the optimal dynamic
investment policy and analyze the hedging demand associated with the new covariate. More generally,
this application shows that smoothed estimates of underlying random processes appear naturally as statevariables when there are forward looking forecasts.

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4.2.2

141

Allmeier Sebastian: Bias of Multiscale Mean Field Models and Averaging Methods
Allmeier Sebastian1 , Gast Nicolas1
1 - INRIA Grenoble-Rhône Alpes (France)

Abstract: The mean field approximation is a powerful technique which has been used in many settings tostudy large-scale stochastic systems. In the case of two-timescale systems, the approximation is obtainedby a combination of scaling arguments and the use of the averaging principle. In this talk, I will present
how to analyze the approximation error of this average’ mean field approach for a two-timescale model(X, Y), where the slow component X describes a population of interacting particles which is fully coupledwith a rapidly changing environment Y.
We show that under relatively mild conditions the ’average’ mean-field approximation has a bias oforder O(1/N ), where N is a scaling factor. We also derive a steady state bias correction term that reduces
the error to order O(1/N 2 ). This refined ’average’ mean-field approximation allows the computation ofan accurate approximation even for small scaling factors, i.e., N ≈ 10–50. We illustrate the developed
framework and accuracy results through an application to a random access CSMA model.

4.2.3

Amanihamedani Alireza: Spatial Matching under Resource Competition
Amanihamedani Alireza1 , Freund Daniel2 , Aouad Ali1
1 - London Business School (United Kingdom)
2 - MIT (United States)

Abstract: Motivated by ride-hailing marketplaces, we present a model of two-sided matching platforms
in a duopoly market with spatial frictions. Both platforms face disjoint streams of rider requests, but theyshare the same resources on the supply side, i.e., drivers multi-home. Due to spatial frictions, supply
efficiency requires an admission control to maintain a buffer of available drivers to balance matching
distances with other supply costs. We consider two types of platform admissions controls, "protection"and "distance thresholds", under a large-market stochastic model. For both controls, we find that a
duopoly equilibrium entails at least one platform "undercutting", by accepting all requests, to gain
market share. When both platforms undercut, the supply buffer is not maintained and inefficiencies
ensue. In contrast, when just one platform undercuts, then the duopoly is asymptotically as efficient as amonopolist. For the protection thresholds, we provide a classifier of market characteristics that delineates
the two regimes and allows us to characterize the prices of anarchy and stability in these settings.

4.2.4

Bhambay Sanidhay: Asymptotic Optimality of Speed-Aware JSQ for
Heterogeneous Service Systems
Bhambay Sanidhay1 , Mukhopadhyay Arpan
1 - University of Warwick (United Kingdom)

Abstract: The Join-the-Shortest-Queue (JSQ) load-balancing scheme is known to minimise the average
delay of jobs in homogeneous systems consisting of identical servers. However, it performs poorly inheterogeneous systems where servers have different processing rates. Finding a delay optimal scheme
remains an open problem for heterogeneous systems. In this work, we consider a speed-aware versionof the JSQ scheme for heterogeneous systems and show that it achieves delay optimality in the fluid
limit. One of the key issues in establishing this optimality result for heterogeneous systems is to showthat the sequence of steady-state distributions indexed by the system size is tight in an appropriately
defined space. The usual technique for showing tightness by coupling with a suitably defined dominantsystem does not work for heterogeneous systems. To prove tightness, we devise a new technique thatuses the drift of exponential Lyapunov functions. Using the non-negativity of the drift, we show that thestationary queue length distribution has an exponentially decaying tail - a fact we use to prove tightness.
Another technical difficulty arises due to the complexity of the underlying state-space and the separationof two time-scales in the fluid limit. Due to these factors, the fluidlimit turns out to be a function of theinvariant distribution of a multi-dimensional Markov chain which is hard to characterise. By using someproperties of this invariant distribution and using the monotonicity of the system, we show that the fluid
limit is has a unique and globally attractive fixed point.

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4.2.5

142

Blomerus Wessel: Structured learning of the optimal replacement rulebeyond Markov settings
Blomerus Wessel1 , Kapodistria Stella1 , Borst Sem1
1 - Eindhoven University of Technology [Eindhoven] (Netherlands)

Abstract: In Markov decision processes, the archetypical point of departure is the Bellman optimality
equations. For such problems, these equations rely on the underlying stochastic model being a Markov
chain. However, the analysis and modelling of real data rarely produces a simple Markov chain model.
Instead, it tends to result in history-dependent models (e.g., autoregressive models) or models withpartial information. This necessitates two extensions of the current solution framework. Firstly, that thetheoretical foundation of the Bellman optimality equations is extended non-Markovian settings. Secondly,that the learning of the underlying model is integrated with the decision analysis. In this presentation
we demonstrate how one can overcome these two challenges. Specifically, we focus on the replacement
problem of an asset such as a technological device or component, and observe its condition, with theobjective to determine when to optimally replace the asset so as to minimize cost. The key assumption
is that the underlying condition (stochastically) renews when the asset is replaced by a good-as-new
asset. In this case, the optimal replacement rule is much more complicated than in the Markoviancase. Despite this complication, we present an algorithm which requires a relatively small number of
iterations to converge to the optimal replacement rule. Furthermore, we show that this framework can beextended to the case where the underlying model is learned in real time whilst also learning the optimal
replacement rule. Lastly, the proposed algorithm is also applicable for optimal stopping problems with a
non-Markovian structure.

4.2.6

Briend Simon: Finding adam under polynomial connectivity assumption
Briend Simon1,2
1 - Universitat Pompeu Fabra [Barcelona] (Spain)
2 - Université Paris-Saclay (France)

Abstract: We study the problem of root finding in growing random graphs under general hypothesis onconnection probabilities. A simple algorithm is introduced, that runs in polynomial time and that outputs
a set of vertices containing the original vertex with high probability. We present its performance in theparticular case of a "general graph" version of the uniform attachment tree. Unlike in the tree, each newvertex connects to several ancestors (and not only one) chosen uniformly at random. We then explain
why this method can be extended to a wide range of growing graphs models and give other examples ofsuch models.

4.2.7

Cheikhi David: On the Statistical Benefits of Temporal Difference
Learning
Cheikhi David1 , Daniel Russo
1 - Columbia University [New York] (United States)

Abstract: Given a dataset on actions and resulting long-term rewards, a direct estimation approachfits value functions that minimize prediction error on the training data. Temporal difference learning(TD) methods instead fit value functions by minimizing the degree of temporal inconsistency between
estimates made at successive time-steps. Focusing on finite state Markov chains, we provide a crisp
asymptotic theory of the statistical advantages of this approach. First, we show that an intuitive inverse
trajectory pooling coefficient completely characterizes the percent reduction in mean-squared error ofvalue estimates. Depending on problem structure, the reduction could be enormous or nonexistent.
Next, we prove that there can be dramatic improvements in estimates of the difference in value-to-go for
two states: TD’s errors are bounded in terms of a novel measure - the problem’s trajectory crossing time
- which can be much smaller than the problem’s time horizon.

4.2.8

Corujo Rodriguez Josué: The standard augmented multiplicative coalescent revisited
Corujo Rodriguez Josué1
1 - Institut de Recherche Mathématique Avancée (France)

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Abstract: The Erd´’os-Rényi random graph is a classic random graph model where vertices are connectedwith a certain probability independently among them. We consider a continuous-time version of thismodel, where all vertices have the same mass and any edge appears with a rate proportional to the
product of the sizes of the vertices. In addition, we do not restrict the graph to be simple, i.e. we allow
the existence of multi-edges and self-loops. It is well-known that the size of the connected componentof this graph follows the standard multiplicative coalescent dynamics. Besides, when accounting for the
additional information consisting in the number of surplus edges, the resulting process follows the socalled standard augmented multiplicative coalescent dynamic. In this talk, we will explain how using abreadth-first exploration process we can prove in a simple way the scaling limit of the standard augmented
multiplicative coalescent. Even if this result is already known, our methods are simpler and more direct
than those encounters in the bibliography. Furthermore, this framework could potentially be extended tothe study of general non-standard augmented multiplicative coalescent as scaling limits of inhomogeneous
random graphs.
This is a work in collaboration with Vlada Limic (CNRS, UniversitÃľ de Strasbourg).

4.2.9

Ertel Sebastian: Ensemble Kalman-Bucy filters for finite and infinite
dimensional signals
Ertel Sebastian1 , Stannat Wilhelm1
1 - Technische Universität Berlin (Germany)

Abstract: In this talk we first derive a (deterministic) mean-field Ensemble Kalman–Bucy filter (EnKBF)
for finite dimensional, nonlinear, continuous time filtering problems as a constant gain approximation to
the Feedback Particle Filter. Other well known versions of the EnKBF are then obtained by replacing
Brownian motions with deterministic inflation terms and vice versa. We then prove the well posednessof this class of mean-field equations via a combination of a fixed point and a partial stopping argument.
Next we investigate the mean-field EnKBF when the signal is given by a nonlinear SPDE, i.e. for infinite
dimensional signals. In this case proving the well posedness requires an adaptation of the fixed point
argument, which makes use of the robustness of the law of total variance with respect to the observation
function. Finally, if time permits, we discuss the analysis of the approximating particle system and a
propagation of chaos result.

4.2.10Jhunjhunwala Prakirt: Join-the-Shortest Queue with Abandonment:
Critically Loaded and Heavily Overloaded Regimes
Jhunjhunwala Prakirt1
1 - Georiga Institute of Technology (United States)

Abstract: We consider a Load Balancing system operating under the well-known Join-the-Shortest Queue(JSQ) policy, where jobs/customers are impatient, and abandon if they do not receive service after some(random) time. In this setting, we study the effect of these abandonments on the steady-state queue
length behavior. In particular, we characterize the ‘distribution of appropriately centered and scaled
steady-state queue length’ (hereafter referred to as limiting distribution) as the abandonment rate goesto zero. Depending on the arrival, service, and abandonment rates, we consider three different regimesof operation, each yielding a different steady-state distribution. The first regime is when the system
is underloaded, where we show that abandonments do not affect the limiting distribution. As a result,
JSQ with Abandonment (JSQ-A) asymptotically behaves like JSQ without abandonments, for which thelimiting distribution is known to be exponential. As the load increases, we move to the second regime,
where abandonments become significant, and there is a critical balance between the arrivals/services
and the abandonments. As such, the system undergoes a phase transition and the limiting distributionchanges from exponential to a truncated normal. As the load increases even further, the system enters
the heavily overloaded regime and the queue lengths become very large. In this case, we show thatthe (centered) steady state queue length distribution converges to a normal distribution. Our results
use the Transform method to establish a one-dimensional differential equation in terms of the MGF(Moment Generating Function) of the steady-state queue lengths, which can be solved to get the limiting
distribution.

4.2.11

Joshi Purva: Performance Evaluation of Intersection Access Mechanisms for Autonomous Vehicles
Joshi Purva1 , Boon Marko1 , Borst Sem1

1 - Department of Mathematics and Computer Science, Eindhoven University of Technology (Netherlands)

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Abstract:
The anticipated launch of fully autonomous vehicles presents an opportunity to develop
and implement novel traffic management systems, such as for urban intersections. Platoon-forming
algorithms, in which vehicles are grouped together with short inter-vehicular distances just before arriving
at an intersection at high speed, seem promising from a capacity-improving standpoint. In this talk, we
present a performance evaluation framework which not only captures the intersection access dynamics via
a queueing model (or, more specifically, a polling model) with multiple customer types, but also explicitly
accounts for the joint optimisation of vehicle trajectories. We focus on deriving computationally fastand interpretable closed-form expressions for safe and efficient vehicle trajectories during the process ofplatoon formation, and show that these closed-form trajectories are equivalent to those obtained via thejoint optimisation process. Additionally, we conduct a numerical study to obtain approximations for thecapacity of an intersection under the platoon-forming framework.

4.2.12

Kalvit Anand: Diffusion limits of multi-armed bandit experiments
under optimism-based policies
Kalvit Anand1 , Zeevi Assaf1
1 - Columbia University (United States)

Abstract: Our work provides new results on the arm-sampling behavior of the classical UCB family of
optimism-based multi-armed bandit algorithms, leading to several important insights. Among these, it
is shown that arm-sampling rates under UCB are asymptotically deterministic, regardless of the problem
complexity. This discovery facilitates new sharp asymptotic characterizations that among other things,
reveal profound distinctions between UCB and Thompson Sampling such as an “incomplete learning"
phenomenon characteristic of the latter.

4.2.13

Karunarathne Wathsala: Scheduling customers in the presence of random customers into stochastic server systems
Karunarathne Wathsala1 , Taylor Peter1 , Fackrell Mark1
1 - University of Melbourne (Australia)

Abstract: We consider an appointment scheduling problem in the presence of random arrivals into
a queueing system with stochastic service times. While there have been discussions about schedulingappointments in stochastic server systems in the literature, little is known about managing appointmentsin the presence of random arrivals. As different queueing systems have disparate objectives to be achieved,
we first consider a multi-objective system where the objectives are to maximise expected revenue, minimise
the total expected customer waiting cost, and minimise the total expected server idle cost. We formulate amathematical model to derive the optimum schedule for a fixed number of customers. Later, we study the
behavior of optimum inter-arrival times and optimum objective function values for individual objectives
and different combinations of objectives. We assume that the service times are exponentially distributed,
and the random customers arrive according to a Poisson process. Further, we assume that the scheduled
customers get non-preemptive priority over random customers, in which the expected waiting times ofscheduled customers can be affected by random customers. The problem is computationally expensive
and was solved using a meta-heuristic search algorithm named covariance matrix adaptation evolutionary
strategy on the University of Melbourne high-performance computing cluster. We show that the system’s
efficiency can be improved by considering random customer arrivals in scheduling. We believe thatthe presented scheduling model and analysis lay a foundation for the problem of static scheduling forscheduling customers in the presence of random customers into a stochastic server system and provide
better insights for relevant enterprises.

4.2.14

Lee Seunghyun: Fluctuations in random field Ising models
Lee Seunghyun1 , Deb Nabarun2 , Mukherjee Sumit1
1 - Department of Statistics, Columbia University (United States)
2 - University of British Columbia (Canada)

Abstract:
The Ising model was originally proposed to explain sharp changes in magnetization forheated magnets, making it essential to examine fluctuations in the average magnetization across different
temperatures. However, previous studies on this topic often assume a specific underlying graph, andthe fluctuations have only been examined on complete graphs for random field Ising models. In thiswork, we answer this question for a broader range of models. We present both quenched and annealed
central limit theorems√for random field Ising models on approximately regular graphs, where the average
degree is larger than n. Notably, our findings suggest that fluctuations at criticality differ from thoseCHAPTER 4. ABSTRACTS OF POSTERS

145

in the Ising model without a random field, both in terms of the magnitude and the resulting limiting
distribution. Our results can be applied to various examples in the litterature, including Erdös-Rényigraphs and block-spin models.

4.2.15

Loubaton Rodolphe: Modelisation of dynamic gene regulatory networks and prediction of biological experiments modifying genes incancerous cells.
Loubaton Rodolphe1 , Champagnat Nicolas1,2 , Vallois Pierre3 , Vallat Laurent4
1 - Institut Élie Cartan de Lorraine (France)
2 - Inria Nancy - Grand Est (France)
3 - Institut Élie Cartan de Lorraine (France)
4 - Interface de Recherche Fondamentale et Appliquée en Cancérologie (France)

Abstract: The cells of the human organism are in constant contact with their environment and changes
in the environment induce adapted responses from the cells. These cellular responses take place throughthe expression of thousands of genes that are activated in a concerted manner and these interactions
are represented as a gene regulatory network. In the case of cancer cells, there are abnormalities in theexpression of certain genes that alter the normal functioning of these gene and protein networks and thusof the gene programmes. Gene programmes correspond to the switching on, control and switching offof the activity of sets of genes as part of the orderly performance of a biological operation that requires
many molecular players. These abnormalities, which are responsible for the malfunctioning of the geneprograms that control cell division, lead to uncontrolled cell proliferation. The general project of thisthesis, which is in line with the work of Laurent Vallat et al. [2, 3], con- sists of modelling the functioningof these gene programmes using temporal data on the expression of genes and proteins. Such a model
will make it possible to identify the genes whose expression modulation will make it possible to improve
cellular behaviour for therapeutic purposes. Laurent Vallat and his team collected healthy and cancerous
cells from control subjects and pa- tients. They then measured, at different times, the expression of genes
by RNA-seq (measurement of the number of RNA copies of more than 20 000 genes) and the expressionof proteins by mass spectrometry (quantity of about 5 000 proteins) after modulation of the cellularenvironment. This modulation is achieved by stimulation of a membrane receptor. First of all, I worked
on a code (R software) that allows a pre-analysis of an RNA-seq data set. This pre-analysis, based on the
R package DESeq2 [1], allows the selection of a subset of genes ”of interest” (about 1000 genes) among all
the genes (about 20 000). I then built a parametric Gaussian statistical model that allows to account forthe dynamics of gene expressions using parameters describing, among others, the interactions between
these “genes of interest”. The statistical inference of the model parameters combines steps of penalized
linear regressions (lasso) and regressions with positivity constraints and constraints on the sum of thecoefficients. I then studied the modelling of biological experiments modifying genes. In practice, thismainly involves the inhibition of gene expression (silencing). We have developed a so-called “mechanistic”
method and a second so-called “conditional” method that make it possible to construct two new models
associated with a biological intervention. Finally, I developed a code for inference and the two prediction
models

4.2.16

Marek Oheim: Risk Class Management in Life Insurance Markets
Marek Oheim1
1 - RPTU Kaiserslautern-Landau (Germany)

Abstract: Grouping customers of different risk types into contracts is important for the stability andthe robustness of an insurance market. In order to analyze the effects of these risk coalitions, we set up
a market model which is inspired by a paper of Sass and Seifried (2014). Afterwards, we provide studies
on the equilibrium premiums of classic life insurance products in monopolistic and competitive insurance
markets. In our model we can show that the market specifications have a high impact on the optimal
choice of different contracts in the insurance market. Regarding markets with capacity constraints serve
as a window for a deeper understanding of the model. Further extensions of the model and its applications
to other insurance types complete the talk.
References:
J. Sass, F.T. Seifried (2014). Insurance markets and unisex tariffs: Is the European Court of Justiceimproving or destroying welfare? Scandinavian Actuarial Journal. 2014, 228-254.

4.2.17

Miguelez Fernando: Price of Anarchy in an Age-aware multi-source
systemMiguelez Fernando1 , Doncel Josu2 , Ayesta Urtzi2,3,4,5

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146

1 - Public University of Navarre, UPNA (Spain)
2 - University of the Basque Country (Spain)
3 - Ikerbasque - Basque Foundation for Science (Spain)
4 - Institut de recherche en informatique de Toulouse (France)
5 - Institut National Polytechnique (Toulouse) (France)

Abstract: In this article we define a system in which K sources generate updates following a Poisson process that are routed to a monitor through a single channel with a hard-preemption policy and exponential
service times. By means of the Price of Anarchy we compare the performance of a competing setting versus a cooperative one. We assume an evaluation function that is the combination of an economic expense
for the server utilisation and the Average Age of Information, a metric that accounts for the freshness of
the last information available at the monitor. In our main result we show that the inneficiency due tothe competition between sources is strongly dependent on the disparity among service rates, and somenumerical experiments support our conclusions.

4.2.18

Pakyari Reza: Spacings-Based Goodness-of-Fit Testing
Pakyari Reza1
1 - Qatar University (Qatar)

Abstract: One of the important steps before performing any parametric statistical analysis is to conduct
a formal goodness-of-fit (GOF) testing. In this talk, we introduce somehow less known GOF testingprocedure based on spacings which has been shown to have larger power in some cases in compare to theconventional Empirical Distribution Function (EDF)-based GOF methods. Indeed, GOF testing basedon spacings cover a large class of tests including the Greenwood’s statistic, the Moran’s statistic and theQuesenberry and Miller’s statistic. GOF tests based on higher order spacings and some suggestions for
new testing methodologies for the case of censored data will also be discussed.

4.2.19Paraggio Paola: A perturbed Bertalanffy-Richards growth: deterministic and stochastic models
Paraggio Paola1 , Di Crescenzo Antonio1 , Torres-Ruiz Francisco2
1 - Department of Mathematics, University of Salerno (Italy)
2 - Department of Statistics and Operational Research, University of Granada (Spain)

Abstract: In [6] Richards introduced a curve suitable to describe asymmetrical growth patterns. Such
curve represents a generalization of the classical logistic function which is characterized by a symmetricbehavior with respect to the inflection point (e.g. [1]) Specifically, in the logistic case, the ratio between
the carrying capacity and the value of the curve at the inflection point is equal to 1/2 whereas in the
Richards case it is equal to (1 + 1/q)q . Hence, the Richards curve seems to be more reasonable to describe
phenomena in which the carrying capacity is not necessarily twice the value at the inflection point. In
this work, we propose a modified Richards-type curve by introducing a perturbation in the growth rate
starting at a critical time instant t∗ . The definition of such generalized model is stimulated by several realsituations in which the growth rate may be modified by external factors as soon as the curve describing
the specific evolution crosses a critical threshold. Think about, for example, the case of oil production of acountry: when the quantity of produced oil is lower than a fixed threshold, the government may decide tosupport new explorations to increase the amount of the production. The modified Richards-type curve isanalyzed both from a deterministic and a stochastic point of view. The modification of the growth rate ischosen so that (i) the properties of the classical Richards curve are preserved and (ii) the new growth rateis greater than the previous one. We provide explicit expressions of the quantities of interest, such as theinflection point, the carrying capacity, the lag time and the maximum specific growth rate. Furthermore,
two stochastic counterparts of the model are considered. More in detail, we define two special birth-deathprocesses (similar to the one proposed in [4]) and a lognormal diffusion process (following the strategyof [2], [3] and [8]). In both cases, the mean of the stochastic process is a modified Richards curve. The
following problems are also addressed: - finding the maximum likelihood estimates of the parameters ofthe diffusion process, - investigating the critical time t∗ when it is regarded as a first-passage-time. In
particular, the maximization of the likelihood function is performed by means of two different heuristic algorithms: Simulated Annealing (as done in [3]) and Ant Lion Optimizer (cf. [5]). Whereas, the R package
fptdApprox (see [7] and [9]) is used to find an approximation of the critical time t∗ . Finally, a simulation study and an application to real data are also presented. This contribution is based on joint works
with A. Di Crescenzo, F. Torres-Ruiz and P. Román-Román. Bibliography [1] Di Crescenzo A., Paraggio P. (2019) Logistic growth described by birth-death and diffusion processes. Mathematics 7 (6) 489.
doi: https://doi.org/10.3390/math7060489 [2] Di Crescenzo A., Paraggio P., Román-Román P., TorresRuiz F. (2021) Applications of the multi-sigmoidal deterministic and stochastic logistic models for plant

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dynamics. Applied Mathematical Modelling, 92, 884–904. https://doi.org/10.1016/j.apm.2020.11.046[3] Di Crescenzo A., Paraggio P., Román-Román P., Torres-Ruiz F. (2022) Statistical analysis and firstpassage-time applications of a lognormal diffusion process with multi-sigmoidal logistic mean. Stat Papers(online first) https://doi.org/10.1007/s00362-022-01349-1 [4] Majee S., Jana S., Khatua A., Kar T. K.
(2022) Growth of single species population: a novel approach. In: Nonlinear Dynamics and Applications.
Proceedings of the ICNDA 2022. pp. 907-915. Springer Proceedings in Complexity. Springer, Cham. doi:
https://doi.org/10.1007/978-3-030-99792-276 [5] Mirjalili S. (2015) The ant lion optimizer. Advances inEngineering Software, 83,80–98. doi: https://doi.org/10.1016/j.advengsoft.2015.01.010 [6] Richards F. J.
(1959) A flexible growth function for empirical use. J Exp Bot 10 (2) 290-300. doi:10.1093/jxb/10.2.290.
[7] Román-Román P., Serrano-Pérez J.J., Torres-Ruiz F. (2008) First-passage-time location function:
application to determine first-passage.time densities in diffusion processes. Comput. Stat. Data Anal.,
52, 4132–4146. doi: https://doi.org/10.1016/j.csda.2008.01.017 [8] Román-Román P., Serrano-Pérez J.
J., Torres-Ruiz F. (2018) Some notes about inference for the lognormal diffusion process with exogenous
factors. Mathematics 6 85 doi: https://doi.org/10.3390/math6050085 [9] Román-Román P., SerranoPérez J.J.M., Torres-Ruiz F. fptdApprox: Approximation of First-Passage-Time Densities for Diffusion
Processes, version 2.2. https://cran.r-project.org/web/packages/fptdApprox/

4.2.20

Sarkovic Andjela: Phase transition for random walks on graphs withadded weighted random matching
Baran Zsuzsanna2 , Hermon Jonathan1 , Šarković Anđela2 , Sousi Perla2
1 - University of British Columbia (Canada)
2 - University of Cambridge (United Kingdom)

Abstract: For a finite graph G = (V, E), we let G∗ be the random graph obtained by placing edges ofweight ε between pairs of vertices of random perfect matching. We establish a phase transition in theoccurrence of cutoff of a weighted random walk on G∗ in terms of the weight ε for graphs of polynomial
volume growth and for expanders. Joint work with Zsuzsanna Baran, Jonathan Hermon and Perla Sousi.

4.2.21

Shadmi Yonatan: Importance sampling in high dimensions
Shadmi Yonatan1 , Simatos Florian2 , Beh Jason
1 - Department of Electrical Engineering - Technion [Haïfa] (Israel)
2 - Institut Supérieur de l’Aéronautique et de l’Espace (France)

Abstract: In this study, we challenge the common belief that the number of samples required for highdimensional importance sampling is exponential in the dimension. We find conditions that allow one
to use importance sampling with number of samples that scales polynomially with the dimension. Our
analysis leverages results by Chatterjee and Diaconis and covers both cases where the importance density
is known in advance and where it must be adaptively estimated from random samples. In the latter
case, the total number of samples required for both estimations of the density and the final integral ispolynomial in the dimension. We also provide a theoretical foundation for understanding the performanceof adaptive importance sampling schemes that rely on projection-based estimations and demonstrate thescalability of the cross entropy algorithm using the insights and techniques developed in this works.

4.2.22Sun Maotong: Towards Safe Extubation Decision Making for Mechanically Ventilated Patients in ICUs: A Deep Constrained Offline
Reinforcement Learning Approach
Sun Maotong1 , Xie Jingui1
1 - Technical University of Munich (Germany)

Abstract: Weaning patients from invasive mechanical ventilation and the regulation of sedation andanalgesia during ventilation are critical decisions in intensive care unit (ICU) management, significantly
affecting patient outcomes and the throughput of ICU, especially during the COVID-19 pandemic. Bothprolonged dependences on mechanical ventilation and premature extubation are associated with an increased risk of complications and higher hospital costs. However, the clinical opinion on the best extubation protocol varies. In this study, we aim to develop a decision support tool that can utilize pre-collectedpatient information to predict time-to-extubation readiness and to have a more personalized extubation strategy for patients in the ICU. To this end, we propose a deep offline constrained reinforcement
learning-based decision support tool to help physicians determine the best action at a given patient statefrom sub-optimal historical ICU data. We evaluate our model using the state-of-art off-policy evaluation(OPE) and show that our model enables substantial improvements in expected health outcomes and inconsistency with relevant practice and safety guidelines.

CHAPTER 4. ABSTRACTS OF POSTERS

4.2.23148

Varma Sushil: A Heavy Traffic Theory of Matching Queues
Varma Sushil Mahavir1 , Maguluri Siva Theja1
1 - Georgia Institute of Technology [Atlanta] (United States)

Abstract: Motivated by emerging applications in online matching platforms and marketplaces, we study
a matching queue. Customers and servers that arrive into a matching queue depart as soon as they arematched. It is known that a state-dependent control is needed to ensure the stability of a matching queue.
However, analytically studying the steady-state behaviour of a matching queue, in general, is challenging.
Therefore, inspired by the heavy-traffic regime in classical queueing theory, we study a matching queuein an asymptotic regime where the state-dependent control decreases to zero. It turns out that there
are two different ways the control can be sent to zero, and we model these using two parameters viz., εis the magnitude scaling parameter that goes to zero and τ is the time scaling parameter that goes toinfinity. Intuitively, ε scales the magnitude of the state-dependent control, and τ is the threshold after
which we modulate the control. We show that depending on the relative rates of ε and τ , there is a phasetransition in the limiting regime. We christen the regime when ετ → 0, the time dominates magnitude(TDM) regime, and the limiting behaviour is a Laplace distribution. The phase transition starts in theregime when, ετ goes to a nonzero constant when the limiting distribution is a Gibbs distribution, and so
we call it the hybrid regime. When ετ → ∞, the limiting distribution converges to a uniform distribution
and we call this the magnitude dominates time (MDT) regime. These results are established using two
related proof techniques. Both the proof techniques generalizes the characteristic function method. The
first one exploits the underlying structure by engineering complex exponential Lyapunov functions, andthe second is a novel inverse Fourier transform method.

4.2.24Wei Zhaoxuan: Online Inventory Control with Partial Backorder
Lim Andrew1 , Wei Zhaoxuan2 , Zhang Hanqin3

1 - Institute of Operations Research and Analytics, National University Singapore (Singapore)2 - Institute of Operations Research and Analytics, National University of Singapore (Singapore)3 - Department of Analytics and Operations, NUS Business School , National University of Singapore (Singapore)

Abstract: We consider a single-product inventory model with patient demand, where the unmet demands
may be either patient and choose to wait (backorder) or lose their patience and choose to leave (lost sale),which can be taken as a hybrid of the classic lost-sales and backorder model and is referred to as partiallybackorder model. With perfect information about the distribution of the demand and customers’ patience,
we identify the (asymptotically) optimal control policy. We then consider a more practical online inventory
control problem where the system manager doesn’t have perfect information about the distribution andthe patience of the demand and only observes the sales. Compared to the literature, our problem has
unique challenges due to the complex nature of the inventory system because (a) the state of the system
is only partially observable as the backorder is unobservable and (b) the sales (the data observed by theagent) could be the exogenously arrived demand and the patient customers. An UCB-type algorithm isthen developed to obtain the near-optimal policy. We prove the regrets of our learning algorithm are(nearly) tight in the planning horizon T, and it can also recover the state-of-art regret bound in thelost-sales model developed in the literature.

4.2.25

Yu Sophie: Constant Regret Primal-Dual Policy for Multi-way Dynamic Matching
Mao Cheng1 , Wu Yihong2 , Xu Jiaming3 , Yu Sophie4
1 - Georgia Institute of Technology (United States)
2 - Yale University (United States)
3 - Duke University (United States)
4 - Duke university [Durham] (United States)

Abstract: We study a discrete-time dynamic multi-way matching model. There are finitely many agent
types that arrive stochastically and wait to be matched. State-of-the-art dynamic matching policies in
the literature require the knowledge of all system parameters to determine an optimal basis of the fluid
relaxation, and focus on controlling the number of waiting agents using only matches in the optimal basis(Kerimov et al., 2021a,b; Gupta, 2021). In this paper, we propose a primal-dual policy that schedule
matches for future arrivals based on an estimator for the dual solution. Our policy does not require theknowledge of optimal bases, and is the first to achieve constant regret at all times under unknown arrival
rates. In addition, we show that when the arrival rates are known, the primal-dual policy achieves the
optimal scaling as the lower-bound described in Kerimov et al. (2021a,b).. Furthermore, we find thatwhen the arrival rates are known, the primal-dual policy can significantly outperform alternative dynamic
matching policies in numerical simulations.

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4.2.26149

Zhe Su: Sample path large deviations for Levy processes and random
walks with log-normal increments
Zhe Su1 , Rhee Chang-Han1 , Zwart Bert2
1 - Northwestern University [Evanston] (United States)
2 - Centrum Wiskunde & Informatica (Netherlands)

Abstract: The past two decades have seen breakthroughs in understanding the general principle that
dictates the asymptotics of the rare events that arise in heavy-tailed systems. Hult, Lindskog, Mikosch,and Samorodnitsky (2005) was the first systematic confirmation of long-standing folklore in the heavy-tailliterature called the principle of a single big jump: that is, the systemwide rare events are often driven by
the catastrophic failure of a single component of the system when the underlying uncertainties are heavytailed, in particular, regularly varying. More recently, Rhee, Blanchet, and Zwart (2019) characterized
the ’catastrophe principle’ in the form of sample path large deviations, which addresses a more generalclass of heavy-tailed rare events far beyond the principle of a single big jump-i.e., the rare events thatare driven by an arbitrary number of unusually large shocks. Bazhba, Blanchet, Rhee, and Zwart(2020)
further extended the large deviations approach to the processes with (heavy-tailed) Weibul increment
distributions, showing that a version of the catastrophe principle still holds in such systems, but theexact characterization is more subtle. Among the three most popular classes of distributions-regularlyvarying, Weibull, and log-normal-for modeling heavy-tailed phenomena, this leaves only the log-normalcase open. In this talk, we establish the sample path large deviations for random walks and Lévy processes
and characterize the catastrophe principle for the log-normal case. We also discuss its implications in the
tail asymptotics of queueing systems with log-normal job sizes.

4.2.27

Zhong Yueyang: Learning the Scheduling Policy in Time-Varying
Multiclass Many Server Queues with Abandonment
Zhong Yueyang1 , Birge John1 , Ward Amy1
1 - The University of Chicago Booth School of Business (United States)

Abstract: We consider a learning variant of a canonical scheduling problem in a multiclass many server
queue with abandonment (specifically, the Mt /M/N + M and the GI/GI/N + GI queues). The objective
is to minimize the long-run average class-dependent expected linear holding and abandonment costs when
the class-dependent model parameters (arrival rates, service rates and abandonment rates) are a priori
unknown. The difficulty is that even when parameters are known, characterizing an optimal schedulingpolicy appears intractable. Fortunately, the simple cµ/θ rule, that prioritizes classes in accordance with astatic ranking that depends on the costs, the service rates, and the abandonment rates, is asymptotically
optimal as the arrival rates and number of servers become large under certain conditions. Then, our task
is to learn the service and abandonment rates well enough to determine an optimal static priority ranking
for the classes, and we can benchmark our performance by defining the regret relative to the cµ/θ rule.
We propose a Learn-Then-Schedule algorithm, which is composed of a learning phase during which point
estimates of the mean service and patience times are formed, and an exploitation phase during which
the cµ/θ rule with empirical mean estimates as a surrogate for actual parameters is followed. It is shownthat the smallest achievable regret for static priority scheduling policies in T periods is ω(log T ), and we
prove that our proposed algorithm achieves a regret upper bound of O(log T ) which matches the lower
bound.

4.2.28

Zur (Schwarz) Elisheva: Priority queues with arrival rate uncertainty
Zur (Schwarz) Elisheva1 , Oz Binyamin1 , Skir Gregory1
1 - The Hebrew University of Jerusalem (Israel)

Abstract: We study an unobservable single-server queue with arrival-rate uncertainty. Customers arrive
according to a Poisson process with a random arrival rate and decide whether to pay for priority. We
characterize the equilibrium customer behavior under different pricing and information disclosure policies.
The model is analyzed under two different assumptions regarding the customers’ degree of rationality.
The first assumes that customers are fully rational and take into account the RASTA (rate-biased ASTA)
phenomenon. The second assumes that customers are naive and base their decision on the PASTA
phenomenon even though the arrival process is not Poisson.

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4.3

Poster session III

4.3.1

Baars Justin: Delayed Hawkes infinite server queue: excitation delayedby sojourn time
Baars Justin1 , Mandjes M. R. H., Laeven Roger
1 - Universiteit van Amsterdam (Netherlands)

Abstract: The Hawkes process is a self-exciting point process where each arrival increases the conditional
intensity instantenously. A variant of this model is introduced that allows for departures, in which theconditional intensity does not increase at arrivals, but at departures from the system. In other words,
arrivals increase the intensity after a delay equal to the service requirements; therefore this model iscalled a delayed Hawkes infinite server queue. In the Markovian case, an explicit recursive procedure
is presented to calculate the dth order moments analytically. Next, this is generalized to a multivariate
setting. Furthermore, in a non-Markovian setting a family of models having sojourn time dependent
excitation is introduced, containing the Hawkes, delayed Hawkes and ephemeral self-exciting processes as
special cases, for which the transform is characterized by a fixed-point of a certain operator, that can beapproximated by an iterative procedure. Next, for the delayed Hawkes process it is shown that if marks
are heavy-tailed — in the sense of being regularly varying — then these heavy tails propagate across thesystem. Finally, I compare the delayed Hawkes process to the regular Hawkes process in the stochastic
ordering, which will enable us to describe heavy-traffic behaviour.

4.3.2

Chenchen Xing: Estimating customer valuation in a service systemwith unobserved balking
Chenchen Xing1 , Taylor Peter1 , Jiesen Wang2
1 - The University of Melbourne (Australia)
2 - Tel Aviv University (Israel)

Abstract: We consider a service system selling goods to strategic customers. Customers are inhomogeneous on the valuation of goods. At their arrival times, customers are informed about the information of
the current system. Customers probably arrive and then immediately balk if the waiting cost is above
the valuation. Those balking customers are not observed by the system manager. The aim of the systemmanager is to estimate customers’ valuation distribution as well as the arrival rate of customers including
balking customers. The modelling framework that we will be working with is a double sided queue with
Poisson streams of goods and customers on both sides. We find the maximum likelihood estimators forthe customer’s valuation and the arrival rate. Consistency of MLEs holds based on simulation results.

4.3.3

Dieleman Nanne: A Pseudo-Gradient Approach for Parameter-free
Optimization of Cost Functions over the Stationary Distribution of
a Discrete Time Markov Chain
Dieleman Nanne A.1 , Heidergott Bernd1 , Berkhout Joost2
1 - Department of Operations Analytics, Vrije Universiteit Amsterdam [Amsterdam] (Netherlands)
2 - Department of Mathematics, Vrije Universiteit Amsterdam [Amsterdam] (Netherlands)

Abstract: Discrete Time Markov Chains (DTMCs) are used to model a wide variety of networks, suchas social, road congestion and hyperlink networks. By optimising these DTMCs, insights and guidelines
are obtained on how the performance of these networks can be improved in practice. In this talk,we consider problems in which (a function of) the stationary distribution of a DTMC is optimised byadjusting the entries of the transition matrix of the DTMC, i.e., we consider a parameter-free setting.
Algorithms exist that can handle linear and quadratic cost functions over the stationary distribution,
but these algorithms do not scale well and cannot handle general costs functions. To optimise a generalcost function, gradient search techniques are required. Unfortunately, gradient search techniques are
not straightforward for this problem, as a violation of the stochasticity of the DTMC transition matrix
during a search step makes it impossible to calculate the stationary distribution and, therefore, the costfunction. In principle, a projected gradient could guarantee the feasibility of the problem throughout
the search, but this is computationally challenging and does not scale well. Therefore, we will present a
pseudo-gradient method that scales well in this talk. More specifically, we develop an algorithm based on
the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm. We give insights into whysolving this optimization problem is notoriously challenging and show the necessary adaptations to the
SPSA algorithm. We compare the developed algorithm to a benchmark algorithm in the case of linear
and quadratic cost functions and show how our algorithm can also be used in the case of general cost

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151

functions. We conclude by providing examples of how this algorithm can be used in practice, for instance,in the ranking of web pages.

4.3.4

Janicka Agnieszka: Buffer content analysis of a two-class tandem fluid
model
Janicka Agnieszka1 , Zwart Bert1
1 - Department of Mathematics and Computer Science [Eindhoven] (Netherlands)

Abstract: We study a tandem fluid model with an On-Off heavy-tailed Source A and a constant Source
B. We assume that Fluid A leaves the system once processed by the first server, while Fluid B continues
to the second buffer. We develop large deviation results for the buffer contents. In particular, we provide
analytic arguments supporting that limn→∞ n−γ ·P(Qi (nT ) ≥ na) = C, i = 1, 2. For Buffer 1, the primarycause of congestion is an On period of order n, as expected based on the Single Big Jump Principle. For
Buffer 2, an additional Off period of order n might be required to cause congestion, depending on model
parameters. The value of γ is determined by the number and type of big’ periods of order n. The
analytical results are complemented by a stochastic simulation of the model, with a rare-event sampling
component that improves simulation efficiency.

4.3.5

Le Corre Thomas: Feature Projection for Optimal Transport
Le Corre Thomas1,2 , Bušić Ana1,2 , Meyn Sean3
1 - Département d’informatique - ENS Paris (France)
2 - Inria de Paris (France)
3 - University of Florida [Gainesville] (United States)

Abstract: Optimal transport is now a standard tool for solving many problems in statistics and machinelearning. The optimal “transport of probability measures” is also a recurring theme in stochastic control and distributed control, where in the latter application the probability measure corresponds to an
empirical distribution associated with a large collection of distributed agents, subject to local and global
control. The goal of this work is to make precise these connections, which inspires new relaxations of optimal transport for application in new and traditional domains. The proposed relaxation replaces a target
measure with a “moment class”: a set of probability measures defined by generalized moment constraints.
This is motivated by applications to control, outlier detection, and to address computational complexity.
The main conclusions are (i) A characterization of the solution is obtained, similar to Kantorovich duality, in which one of the dual functions in the classical theory is replaced by a linear combination of thefeatures defining the generalized moments. Hence the dimension of the optimization problem coincides
with the number of constraints, even with an uncountable state space; (ii) By introducing regularizationin the form of relative entropy, the solution can be interpreted as replacing a maximum with a soft-max
in the dual; (iii) In applications such as control for which it is not known a-priori if the moment class is
non-empty, a relaxation is proposed whose solution admits a similar characterization; (iv) The gradientof the dual function can be expressed in terms of the expectation of the features under a tilted probability
measure, which motivates Monte-Carlo techniques for computation. We illustrate the approach on two
applications: i) Optimal charging of EV arriving at a car park ii) Signal tracking by controlling theconsumption of a set of consumers in an electricity network.

4.3.6

Levering Nikki: Externalities in the M/G/1 queue: LCFS-PR versus
FCFS
Jacobovic Royi1 , Levering Nikki2 , Boxma Onno3
1 - University of Amsterdam (Netherlands)
2 - University of Amsterdam (Netherlands)
3 - Eindhoven University of Technology (Netherlands)

Abstract: Externalities are the costs that a user of a common resource imposes on others. In a stable
M/G/1 system, the externalities created by an arriving customer with known service requirement equal
the total waiting time that others will save if she would reduce her service requirement to zero. Naturally,
these externalities are stochastic, and the corresponding analysis relies heavily on the underlying service
discipline. In this talk, we compare the externalities under the last-come first-served with preemption(LCFS-PR) and first-come first-served (FCFS) service distributions. Specifically, we establish a joint
decomposition for the externalities under LCFS-PR and FCFS in terms of a bivariate compound Poissonprocess. This decomposition can be used to derive several other results regarding the externalities:
moments, asymptotic approximations as the service requirement goes to infinity, asymptotics of the taildistribution, and a functional central limit theorem.

CHAPTER 4. ABSTRACTS OF POSTERS

4.3.7

152

Loeser Eva: Fluid Limit for a Stochastic Model of Enzymatic Processing with General Distributions
Loeser Eva1 , Williams Ruth1
1 - University of California [San Diego] (United States)

Abstract: We consider a stochastic chemical reaction system arising as a model for enzymatic processing
in a cell. This can also be thought of as a multiserver multiclass queue with reneging operating under the
random order of service discipline. Stochastic primitives for the model such as production/interarrival
times, processing/service times, and lifetimes are assumed to be generally distributed. We establish a
fluid limit for a measure valued process that keeps track of the remaining lifetime for each entity in thesystem. We prove uniqueness for fluid model solutions under mild conditions and study the asymptotic
behavior of fluid model solutions as time goes to infinity. This poster is based on joint work with Ruth
Williams.

4.3.8

Luo Yuwei: A Geometric Approach to Improve the Worst Case Performance of Thompson Sampling
Luo Yuwei1 , Bayati Mohsen1
1 - Stanford Graduate School of Business [Stanford] (United States)

Abstract: In this paper, we study Thompson Sampling (TS) for the stochastic linear bandit problem.
TS is a popular policy to use in practice partly due to its strong empirical performance. In addition,
it is known that TS achieves minimax√ Bayesian regret. However, its worst-case regret suffers from an
extra multiplicative factor of order d, which is recently shown to be unavoidable. Motivated by thisdiscrepancy between the worst-case versus typical performance of TS, we derive an instance-dependent
regret bound for TS that can be calculated efficiently in a “data-driven” manner through a geometricapproach. This result enables us to detect and “course-correct” if TS performs poorly in any specificproblem instance. We showthat a variant of TS based on our approach enjoys the minimax optimal√
worst-case regret of Õ(d T ) while keeping most of the properties of TS. Simulation results are presented
to validate our insights and to compare our algorithm with the baselines.

4.3.9

Mcarthur Harry: Preserving Privacy while Publishing Information
Mcarthur Harry1 , Smith-Miles Kate1 , Taylor Peter1
1 - University of Melbourne (Australia)

Abstract: The central goal of data-dissemination agencies around the world is to collect and publish
information about individuals in a population to inform important policy decisions and research objectives. Can we ensure the privacy of the individuals is maintained while retaining the utility of the data?
The current privacy preserving techniques used in practice rely on injecting noise into the statistics prior
to publishing. In order to quantify the level of leakage a given technique has, we can consider what an
attacker can learn from the released information, or equivalently what they can reconstruct about theunderlying database. Anything that can be learned about an individual is a potential violation of privacy.
We discuss the design of different reconstruction methods for the purpose of informing the decision on
how best to inject this noise, or implement other privacy preserving techniques — optimising the utilityof the data subject to certain privacy constraints.

4.3.10

Mohammadi Narges: Efficient Discovery of Cost-effective Policies in
Sequential, Medical Decision-Making Problems
Mohammadi Narges1 , Skandari Reza2 , Shah Anand3
1 - PhD Student, Imperial College Business School (United Kingdom)
2 - Assistant Professor, Imperial College Business School (United Kingdom)
3 - School of Public Health, Imperial College (United Kingdom)

Abstract: Cost-effectiveness analysis is widely used by policymakers to prioritize interventions that improve a population’s health. Net monetary benefit (NMB) is a metric used for the comparison of medicalcare strategies, which converts an intervention’s health-benefits to monetary value using the willingness
to pay (WTP) as the exchange rate. There is no universally accepted value for WTP, and its value
affects the optimal choice of strategies. In this paper, we propose constructing a comprehensive menu
of cost-effective strategies for the policymaker to choose from and develop a novel algorithm that efficiently generates the cost-effectiveness frontier. The algorithm is based on successive application of theCHAPTER 4. ABSTRACTS OF POSTERS

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incremental cost-effectiveness ratio (ICER) minimization problem. We apply our modelling framework
to design cost-effective hearing loss screening strategies for patients with cystic fibrosis (CF) disease.
We build a stochastic optimization problem based on a hidden Markov reward model to optimally decide the timing and modality of audiometry. We solve the associated NMB and ICER optimization
problems using a partially observable Markov decision process (POMDP) and a constrained, non-linear
POMDP model, respectively. We build a data-driven model using evidence from the literature and use agrid-based approximation method to solve the problem numerically. We develop multiple heuristic andapproximate policies and evaluate their performance against the optimal policy based on several metrics
using a simulation model. The policymaker can use the simulation results to decide the trade-off between
performance and ease of implementation of the various policies. We prove several theoretical propertiesof the proposed solution methods.

4.3.11

Pijnappel Tom: Optimal Deployment of Drone Base Stations in Wireless Networks
Pijnappel Tom1 , Van Den Berg Hans2 , Borst Sem1 , Litjens Remco3,4
1 - Eindhoven University of Technology (Netherlands)
2 - University of Twente (Netherlands)
3 - TNO (Netherlands)
4 - Technische Universiteit Delft (Netherlands)

Abstract: Reliable mobile communications is of critical importance, and should be maintained even incase of extremely crowded events or emergency scenarios. In such scenarios the deployment of dronemounted base stations offers an agile and cost-efficient way to sustain coverage and/or provide capacity
relief. In this talk we present a stochastic framework based on multi-class loss systems to estimate theblocking and coverage probabilities of drone-assisted wireless networks using information that is readily
available to network operators. We demonstrate how this framework can be used to determine theminimum required number of drones and their corresponding locations for a given target performancelevel.

4.3.12Ray Rounak: Percolation on Preferential Attachment Models
Ray Rounak1 , Van Der Hofstad Remco, Hazra Rajat Subhra, Garavaglia Alessandro

1 - Department of Mathematics and Computer Science, Eindhoven University of Technology (Netherlands)

Abstract: We study the bond percolation on the preferential attachment models. We have explicitly
identified the critical percolation threshold for preferential attachment models with m(≥ 2) out-edges andany affinity parameter δ > −m. We have shown that the preferential attachment models are large-set
expander and the survival probability is continuous at the critical percolation threshold. The work of
Alimohammadi, Borga and Saberi (2021) suggests that for such cases, the critical percolation thresholdfor the graphs are that of the local limit. In our previous work (2022), we have shown that Pólya point
tree is the local limit of a large class of preferential attachment models. For the case of negative δ, wehave shown that the percolation threshold for Pólya point tree is 0. For positive values of δ, the criticalpercolation threshold turns out to be the spectral radius of the offspring generator of the Pólya point
tree.
References:• Yeganeh Alimohammadi and Christian Borgs and Amin Saberi (2021), Locality of Random Digraphs
on Expanders• Alessandro Garavaglia, Rajat Hazra, Remco van der Hofstad, Rounak Ray (2022), Universality of
the local limit of preferential attachment models

4.3.13

Shah Abhin: On counterfactual inference with unobserved confounding
Shah Abhin1,2,3 , Dwivedi Raaz1,3,4 , Shah Devavrat1,3 , Wornell Gregory2,3,5

1 - Laboratory for Information and Decision Systems - Massachusetts Institute of Technology (United States)
2 - Research Laboratory - Massachusetts Institute of Technology (United States)
3 - MIT Institute for Data, Systems, and Society [Cambridge, MA] (United States)
4 - Harvard University Statistics Department (United States)
5 - MIT Computer Science & Artificial Intelligence Lab (United States)

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Abstract: Given an observational study with n independent but heterogeneous units, our goal is to learn
the counterfactual distribution for each unit using only one p-dimensional sample per unit containing
covariates, interventions, and outcomes. Specifically, we allow for unobserved confounding that introduces
statistical biases between interventions and outcomes as well as exacerbates the heterogeneity across units.
Modeling the underlying joint distribution as an exponential family, we reduce learning the unit-levelcounterfactual distributions to learning n exponential family distributions with heterogeneous parameters
and only one sample per distribution. We introduce a convex objective that pools all n samples to jointly
learn all nparameter vectors, and provide a unit-wise mean squared error bound that scales linearlywith the metric entropy of the parameter space. For example, when the parameters are s-sparse linear
combination of k known vectors, the error is O(s log k/p). En route, we derive sufficient conditions forcompactly supported distributions to satisfy the logarithmic Sobolev inequality. As an application of theframework, our results enable consistent imputation of sparsely missing covariates.

4.3.14

Van Santvoort Mike: Mathematically mapping the network of cells in
the tumor microenvironment

Van Santvoort Mike1 , Lapuente-Santana ÃŞscar1 , Finotello Francesca2 , Van Der Hoorn Pim1 , Eduati
Federica1
1 - Eindhoven University of Technology [Eindhoven] (Netherlands)
2 - Universität Innsbruck [Innsbruck] (Austria)

Abstract: In a malignant cancer, tumor cells exploit communication with immune cells to build resistance to one’s own self-defense mechanisms. They break down and rebuild the communication networkof interacting cells to misuse it for their own benefit. Treatment strategies like immunotherapy aim tointerfere with this pro-tumor communication network, but for such treatments to be effective, it is important to map out what the cell-cell communication network in a patient’s tumor looks like. To this end
a lot of data about interacting proteins can be extracted from a patient’s body, but it is often unclear
how this data can be mapped consistently to a digraph of cells (nodes) and protein interactions (arcs).
To intuitively bridge the gap between prior knowledge and a patient’s cell-cell communication network,
we propose a random graph model and analyze its properties, such that they can be used as predictive
features of a patient’s cancer. In our model nodes have types and arcs have colors. A type and color
distribution is given as input, which encodes the probability that a uniformly chosen node/arc has acertain type/color. Additionally, a ruleset is given that fixes which arc colors can possibly connect to agiven node type. Graphs of a fixed number of nodes and arcs are generated by first giving each node/arc a
type/color independently of the others. Thereafter, each arc is placed uniformly at random between two
nodes that can accept the arc according to the ruleset. We mathematically analyze the model by coupling
probabilities of monotone events to probabilities in the inhomogeneous random digraph model. Then,
we use recent literature by Cao et al. to translate properties of the inhomogeneous random digraph toour model. Examples of properties we investigate include the degree distribution, wedge/triangle counts,
and the emergence of a giant strongly connected component.

4.3.15

Van Werde Alexander: Universality-based concentration for sums ofdependent random matrices
Van Werde Alexander1
1 - Eindhoven University of Technology (Netherlands)

Abstract: We establish concentration inequalities for sums of dependent random matrices. Our results
allow for two types of dependencies: First, we consider a model where the summands are generated bya Markov chain. Second, we consider a model where the summands are deterministic matrices scaled byrandom coefficients. The leading-order term in our results is asymptotically sharp and is identified by aquantity from free probability theory. We discuss applications related to community detection in block
Markov chains.

4.3.16

Wellalage Achini: A Monte Carlo simulation-based simulated annealing algorithm for predicting the minimum staffing requirement at a
blood donor centre
Wellalage Achini1 , Fackrell Mark1 , Zhang Lele (joyce)1
1 - The University of Melbourne (Australia)

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Abstract: Australian Red Cross Lifeblood collects blood from non-remunerated voluntary donors. Thus,
it is important to ensure that donors experience good service so they will return to donate blood again.
Donor experience is adversely influenced by prolonged waiting times, but they may be reduced by determining the staffing demand over the day. In this talk, I propose a Monte-Carlo simulation-based
simulated annealing algorithm that seeks the minimum number of employees to meet demand over a
single day while ensuring the system’s predicted average waiting time does not exceed a specified threshold. To enhance the efficiency of our simulated annealing algorithm, we develop a novel neighbourhood
search method based on the staff occupancy levels. We use data from four different Australian Red Cross
Lifeblood donor centres, demonstrating that our methodology can be adapted to any donor centre to
determine the minimum staffing demand. Since these staffing demands ensure the donor waiting timetarget is met for each donor centre, they have the potential to improve both donor and staff satisfaction
as well as streamline the donor flow.

4.3.17

Yeung Lane Chun: A non-asymptotic perspective on mean field control via log-concavity
Yeung Lane Chun1 , Lacker Daniel1 , Mukherjee Sumit2

1 - Department of Industrial Engineering and Operations Research, Columbia University (United States)
2 - Department of Statistics, Columbia University (United States)

Abstract: We study a class of stochastic control problems in which a large number of players cooperatively
choose their drifts to maximize an expected reward minus a quadratic running cost. For a broad classof potentially asymmetric rewards, we show that there exist approximately optimal controls which aredecentralized, in the sense that each player’s control depends only on its own state and not the states ofthe other players. Moreover, the optimal decentralized controls can be constructed non-asymptotically,
without reference to any mean field limit. These results descend from a broader framework inspired by thetheory of nonlinear large deviations of Chatterjee-Dembo, for which we offer an efficient non-asymptotic
perspective in log-concave settings based on functional inequalities. Joint work with Daniel Lacker andSumit Mukherjee.

4.3.18Zhang Kelly: Inference after Using RL to Learn Across Users for
Digital Interventions
Zhang Kelly1
1 - Harvard University [Cambridge] (United States)

Abstract: Online reinforcement learning (RL) algorithms are a key tool for personalizing decision-making
for digital interventions, as these algorithms can use previously collected data from users to learn andimprove future treatment decisions. We provide methods to perform a variety of statistical analyses using
data collected by RL and other adaptive sampling algorithms for digital interventions. In this work, we
focus on data collected by online RL algorithms that can learn across users, i.e., use the data of multiple
users to learn and inform treatment decisions. This data type is important since highly noisy outcomes(as is the case for digital interventions) mean RL algorithms that learn using the data of multiple users
can significantly reduce noise and learn faster. At the same time, this data type is challenging to develop
inferential theory for because online RL algorithms that combine data across induce dependence between
the collected user data trajectories. We develop a general inferential approach for this non-i.i.d. data
type that allows one to, for example, construct confidence intervals for time-varying treatment effects
and perform off-policy analyses.

4.3.19Zhu Feng: Stochastic Multi-armed Bandits: Trade-off among Optimality, Consistency, and Tail Risk
Simchi-Levi David1 , Zheng Zeyu2 , Zhu Feng1
1 - Massachusetts Institute of Technology (United States)
2 - University of California, Berkeley (United States)

Abstract: We study the stochastic K-armed bandit and fully characterize the interplays among threedesired properties for policy design: worst-case optimality, instance-dependent consistency, and lighttailed risk. We show how the order of expected regret exactly affects the decaying rate of the regret tailprobability for both the worst-case and instance-dependent scenario. A novel policy is proposed suchthat for any given α ∈ [1/2, 1) and β ∈ [0, 1), our policy achieves a worst-case expected regret of Õ(T α )and instance-dependent expected regret of Õ(T β ), while enjoys a probability of incurring an Ω(T δ ) regret

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that decays exponentially with a polynomial T term. Such decaying rate is proved to be best achievable.
Our proposed policy achieves a delicate balance between doing more exploration at the beginning of thetime horizon and doing more exploitation when approaching the end, compared to standard policies suchas Successive Elimination and Upper Confidence Bound. A brief account of numerical experiments is
conducted to illustrate the theoretical findings. Finally, we extend our proposed policy design to (1)a stochastic multi-armed bandit setting with adversarial baseline rewards (2) a stochastic linear bandit
setting. Our results reveal insights on the trade-off between expected regret and tail risk for both worstcase and instance-dependent scenario, indicating that more sub-optimality and inconsistency leaves spacefor more light-tailed risk of incurring large regret.

4.3.20Zhou Han: Optimal starting for Renal Replacement Therapy in Intensive Care Unit
Zhou Han1 , Xie Jingui1
1 - Technische Universität München = Technical University of Munich (Germany)

Abstract: Whether and when to initiate renal replacement therapy (RRT) is a controversial question in
critical care. Benefits of delaying therapy include enabling a substantial number of patients to recoverfrom acute kidney injury without undergoing RRT, and avoiding exposure to an unnecessary therapy with
its negative side effects and costs. On the other hand, early therapy could improve electrolyte and fluidbalance, enhance the removal of toxins, and thereby prevent potential life-threatening harm. Thus, there
is a trade-off between short-term benefits and long-term recovery risks. Aiming to identify a treatment
policy to minimize the mortality of a patient, we develop Markov decision processes. We prove conditions
that establish the structural properties of the optimal solution. To estimate model parameters, we utilize
instrumental variables and prediction methods.