pwc-archive/papers-with-abstracts huggingface dataset:
Crossover Can Guarantee Exponential Speed-Ups in Evolutionary Multi-Objective Optimisation
Authors: Duc-Cuong Dang, Andre Opris, Dirk Sudholt
Date: 2023-01-31 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary algorithms are popular algorithms for multiobjective optimisation (also called Pareto optimisation) as they use a population to store trade-offs between different objectives. Despite their popularity, the theoretical foundation of multiobjective evolutionary optimisation (EMO) is still in its early development. Fundamental questions such as the benefits of the crossover operator are still not fully understood. We provide a theoretical analysis of the well-known EMO algorithms GSEMO and NSGA-II to showcase the possible advantages of crossover: we propose classes of "royal road" functions on which these algorithms cover the whole Pareto front in expected polynomial time if crossover is being used. But when disabling crossover, they require exponential time in expectation to cover the Pareto front. The latter even holds for a large class of black-box algorithms using any elitist selection and any unbiased mutation operator. Moreover, even the expected time to create a single Pareto-optimal search point is exponential. We provide two different function classes, one tailored for one-point crossover and another one tailored for uniform crossover, and we show that immune-inspired hypermutations cannot avoid exponential optimisation times. Our work shows the first example of an exponential performance gap through the use of crossover for the widely used NSGA-II algorithm and contributes to a deeper understanding of its limitations and capabilities.
Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding
Authors: Lan Tang, Xiaxi Li, Jinyuan Zhang, Guiying Li, Peng Yang, Ke Tang
Date: 2023-01-31 00:00:00
Tasks: Atari Games, Evolutionary Algorithms, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE.
A Counterfactual Collaborative Session-based Recommender System
Authors: Wenzhuo Song, Shoujin Wang, Yan Wang, Kunpeng Liu, Xueyan Liu, Minghao Yin
Date: 2023-01-31 00:00:00
Tasks: Causal Inference, counterfactual, Counterfactual Inference, Recommendation Systems

Most session-based recommender systems (SBRSs) focus on extracting information from the observed items in the current session of a user to predict a next item, ignoring the causes outside the session (called outer-session causes, OSCs) that influence the user's selection of items. However, these causes widely exist in the real world, and few studies have investigated their role in SBRSs. In this work, we analyze the causalities and correlations of the OSCs in SBRSs from the perspective of causal inference. We find that the OSCs are essentially the confounders in SBRSs, which leads to spurious correlations in the data used to train SBRS models. To address this problem, we propose a novel SBRS framework named COCO-SBRS (COunterfactual COllaborative Session-Based Recommender Systems) to learn the causality between OSCs and user-item interactions in SBRSs. COCO-SBRS first adopts a self-supervised approach to pre-train a recommendation model by designing pseudo-labels of causes for each user's selection of the item in data to guide the training process. Next, COCO-SBRS adopts counterfactual inference to recommend items based on the outputs of the pre-trained recommendation model considering the causalities to alleviate the data sparsity problem. As a result, COCO-SBRS can learn the causalities in data, preventing the model from learning spurious correlations. The experimental results of our extensive experiments conducted on three real-world datasets demonstrate the superiority of our proposed framework over ten representative SBRSs.
Joint calibration to SPX and VIX options with signature-based models
Authors: Christa Cuchiero, Guido Gazzani, Janka Möller, Sara Svaluto-Ferro
Date: 2023-01-30 00:00:00

We consider a stochastic volatility model where the dynamics of the volatility are described by a linear function of the (time extended) signature of a primary process which is supposed to be a polynomial diffusion. We obtain closed form expressions for the VIX squared, exploiting the fact that the truncated signature of a polynomial diffusion is again a polynomial diffusion. Adding to such a primary process the Brownian motion driving the stock price, allows then to express both the log-price and the VIX squared as linear functions of the signature of the corresponding augmented process. This feature can then be efficiently used for pricing and calibration purposes. Indeed, as the signature samples can be easily precomputed, the calibration task can be split into an offline sampling and a standard optimization. We also propose a Fourier pricing approach for both VIX and SPX options exploiting that the signature of the augmented primary process is an infinite dimensional affine process. For both the SPX and VIX options we obtain highly accurate calibration results, showing that this model class allows to solve the joint calibration problem without adding jumps or rough volatility.
Forex Trading Strategy That Might Be Executed Due to the Popularity of Gotobi Anomaly
Authors: Hiroki Bessho, Takanari Sugimoto, Tomoya Suzuki
Date: 2023-01-29 00:00:00

Our previous research has confirmed that the USD/JPY rate tends to rise toward 9:55 every morning in the Gotobi days, which are divisible by five. This is called the Gotobi anomaly. In the present study, we verify the possible trading strategy and its validity under the condition that investors recognize the existence of the anomaly. Moreover, we illustrate the possibility that the wealth of Japanese companies might leak to FX traders due to the arbitrage opportunity if Japanese companies blindly keep making payments in the Gotobi days as a business custom.
Can we infer microscopic financial information from the long memory in market-order flow?: a quantitative test of the Lillo-Mike-Farmer model
Authors: Yuki Sato, Kiyoshi Kanazawa
Date: 2023-01-31 00:00:00

In financial markets, the market order sign exhibits strong persistence, widely known as the long-range correlation (LRC) of order flow; specifically, the sign correlation function displays long memory with power-law exponent $\gamma$, such that $C(\tau) \propto \tau^{-\gamma}$ for large time-lag $\tau$. One of the most promising microscopic hypotheses is the order-splitting behaviour at the level of individual traders. Indeed, Lillo, Mike, and Farmer (LMF) introduced in 2005 a simple microscopic model of order-splitting behaviour, which predicts that the macroscopic sign correlation is quantitatively associated with the microscopic distribution of metaorders. While this hypothesis has been a central issue of debate in econophysics, its direct quantitative validation has been missing because it requires large microscopic datasets with high resolution to observe the order-splitting behaviour of all individual traders. Here we present the first quantitative validation of this LFM prediction by analysing a large microscopic dataset in the Tokyo Stock Exchange market for more than nine years. On classifying all traders as either order-splitting traders or random traders as a statistical clustering, we directly measured the metaorder-length distributions $P(L)\propto L^{-\alpha-1}$ as the microscopic parameter of the LMF model and examined the theoretical prediction on the macroscopic order correlation: $\gamma \approx \alpha - 1$. We discover that the LMF prediction agrees with the actual data even at the quantitative level. Our work provides the first solid support of the microscopic model and solves directly a long-standing problem in the field of econophysics and market microstructure.
HJM Local Volatility Model
Authors: V. M. Belyaev
Date: 2023-01-31 00:00:00
Tasks: model, Sensitivity

Local Volatility (LV) is a powerful tool for market modeling, enabling the generation of arbitrage-free scenarios calibrated to all European options. To implement LV, we need to interpolate and extrapolate option prices. This approach is significantly faster and more accurate than any parameterized model. The implementation is demonstrated specifically for interest rate swaptions and caplets. A key component of this method is the Small Volatility Approximation within the HJM interest rate model, which is used to calculate sensitivity of forward bond volatility. These calculations are deterministic and fast, with excellent calibration accuracy. A detailed description of the calibration procedure is provided.
View fusion vis-à-vis a Bayesian interpretation of Black-Litterman for portfolio allocation
Authors: Trent Spears, Stefan Zohren, Stephen Roberts
Date: 2023-01-31 00:00:00

The Black-Litterman model extends the framework of the Markowitz Modern Portfolio Theory to incorporate investor views. We consider a case where multiple view estimates, including uncertainties, are given for the same underlying subset of assets at a point in time. This motivates our consideration of data fusion techniques for combining information from multiple sources. In particular, we consider consistency-based methods that yield fused view and uncertainty pairs; such methods are not common to the quantitative finance literature. We show a relevant, modern case of incorporating machine learning model-derived view and uncertainty estimates, and the impact on portfolio allocation, with an example subsuming Arbitrage Pricing Theory. Hence we show the value of the Black-Litterman model in combination with information fusion and artificial intelligence-grounded prediction methods.
Utility-based indifference pricing of pure endowments in a Markov-modulated market model
Authors: Alessandra Cretarola, Benedetta Salterini
Date: 2023-01-31 00:00:00

In this paper we study exponential utility indifference pricing of pure endowment policies in a stochastic-factor model for an insurance company, which can also invest in a financial market. Specifically, we propose a modeling framework where the hazard rate is described by an observable general diffusion process and the risky asset price evolves as a jump diffusion affected by a continuous-time finite-state Markov chain representing regimes of the economy. Using the classical stochastic control approach based on the Hamilton-Jacobi-Bellman equation, we describe the optimal investment strategies with and without the insurance derivative and characterize the indifference price in terms of a classical solution to a linear PDE. We also provide its probabilistic representation via an extension of the Feynman-Kac formula show that it satisfies a final value problem. Furthermore, we also discuss the indifference price for a portfolio of insurance policies and for a term life insurance. Finally, some numerical experiments are performed to address sensitivity analyses.
Wavelet Analysis for Time Series Financial Signals via Element Analysis
Authors: Nathan Zavanelli
Date: 2023-01-30 00:00:00
Tasks: Time Series, Time Series Analysis

The method of element analysis is proposed here as an alternative to traditional wavelet-based approaches to analyzing perturbations in financial signals by scale. In this method, the processes that generate oscillations in financial signals are modelled as scaled, shifted, and isolated events that produce ripples of various frequencies across a sea of noise as opposed to a simple sinusoidal or mixed frequency oscillation or an impulse. This allows one to directly estimate the wavelet parameters derived only from the generating functions, rejecting spurious perturbations driven by noise or extraneous factors. Financial signals may then be reconstructed based on a finite set of generators localized in time and frequency. This method offers a marked advantage compared to traditional econometric tools because it directly targets the generators of oscillations. Furthermore, the choice of the Morse wavelet allows for wide latitude in capturing a broad set of diverse generators. In this work, the basic mathematical principles underlying element analysis are presented, and the method is applied to the study of variance in financial data, where the advantages of element analysis over traditional wavelet techniques is demonstrated. Specifically, in the example analysis of inflation expectations, element analysis shows a clear ability to distinguish between oscillations formed by noise and those formed by generators logically matched to historical events.
Expectation Propagation for Continuous Time Bayesian Networks
Authors: Uri Nodelman, Daphne Koller, Christian R. Shelton
Date: 2012-07-04 00:00:00

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is intractable. We address the problem of approximate inference, allowing for general queries conditioned on evidence over continuous time intervals and at discrete time points. We show how CTBNs can be parameterized within the exponential family, and use that insight to develop a message passing scheme in cluster graphs and allows us to apply expectation propagation to CTBNs. The clusters in our cluster graph do not contain distributions over the cluster variables at individual time points, but distributions over trajectories of the variables throughout a duration. Thus, unlike discrete time temporal models such as dynamic Bayesian networks, we can adapt the time granularity at which we reason for different variables and in different conditions.
Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
Authors: Uri Nodelman, Christian R. Shelton, Daphne Koller
Date: 2012-07-04 00:00:00

Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters and structure of a CTBN from partially observed data. We show how to apply expectation maximization (EM) and structural expectation maximization (SEM) to CTBNs. The availability of the EM algorithm allows us to extend the representation of CTBNs to allow a much richer class of transition durations distributions, known as phase distributions. This class is a highly expressive semi-parametric representation, which can approximate any duration distribution arbitrarily closely. This extension to the CTBN framework addresses one of the main limitations of both CTBNs and DBNs - the restriction to exponentially / geometrically distributed duration. We present experimental results on a real data set of people's life spans, showing that our algorithm learns reasonable models - structure and parameters - from partially observed data, and, with the use of phase distributions, achieves better performance than DBNs.
Integrated optimization of railway freight operation planning and pricing based on carbon emission reduction policies
Authors: Lin Li a, Xiaoqiang Zhang a, B, C, *
Date: 2020-02-08 00:00:00
Proceeding: Journal of Cleaner Production 2020 2
Tasks: Management

Cargo transportation is one of the major sources of carbon emissions. To reduce carbon emissions from
inland freight, one way is to encourage rail transportation instead of truck transportation. In this paper, a
new approach combining railway operation management, revenue management, and policy formulation
is proposed to increase the market share of railway freight and reduce carbon emissions. Through the
integrated optimization of operation planning and pricing, the competitiveness of railway freight services is improved. The carbon tax policy acting on the demand-side can affect consigners’ choice
behavior. The low-carbon subsidy policy acting on the supply-side can encourage rail transport. Then a
bi-level multi-objective model is proposed to achieve the trade-off among the three stakeholders (the
government, rail service operators, and consigners). The complexity of the bi-level multi-objective
problem motivates the development of a hybrid optimization algorithm that combines the Nondominated sorting genetic algorithm III (NSGA-III) and the Descent algorithm based on sensitivity
analysis (SAB). Through a real case based on a rail service network with nine nodes in China, the rationality of the proposed methodology is verified. The results show that the approach proposed can
achieve a 39.27% modal shift (MS) from road to rail, as well as a 37.09% carbon emission reduction.
Efficient and Effective SPARQL Autocompletion on Very Large Knowledge Graphs
Authors: Hannah Bast, Johannes Kalmbach, Theresa Klumpp, Florian Kramer, Niklas Schnelle
Date: 2022-10-17 00:00:00
Proceeding: International Conference on Information & Knowledge Management (CIKM) 2022 10
Tasks: Knowledge Graphs

We show how to achieve fast autocompletion for SPARQL queries on very large knowledge graphs. At any position in the body of a SPARQL query, the autocompletion suggests matching subjects, predicates, or objects. The suggestions are context-sensitive and ranked by their relevance to the part of the query already typed. The suggestions can be narrowed down by prefix search on the names and aliases of the desired subject, predicate, or object. All suggestions are themselves obtained via SPARQL queries. For existing SPARQL engines, these queries are impractically slow on large knowledge graphs. We present various algorithmic and engineering improvements of an open-source SPARQL engine such that these queries are executed efficiently. We evaluate a variety of suggestion methods on three large knowledge graphs, including the complete Wikidata. We compare our results with two widely used SPARQL engines, Virtuoso and Blazegraph. Our code, benchmarks, and complete reproducibility materials are available on https://ad.cs.uni-freiburg.de/publications.
Physics-informed Reduced-Order Learning from the First Principles for Simulation of Quantum Nanostructures
Authors: Martin Veresko, Ming-Cheng Cheng
Date: 2023-01-31 00:00:00

Multi-dimensional direct numerical simulation (DNS) of the Schr\"odinger equation is needed for design and analysis of quantum nanostructures that offer numerous applications in biology, medicine, materials, electronic/photonic devices, etc. In large-scale nanostructures, extensive computational effort needed in DNS may become prohibitive due to the high degrees of freedom (DoF). This study employs a reduced-order learning algorithm, enabled by the first principles, for simulation of the Schr\"odinger equation to achieve high accuracy and efficiency. The proposed simulation methodology is applied to investigate two quantum-dot structures; one operates under external electric field, and the other is influenced by internal potential variation with periodic boundary conditions. The former is similar to typical operations of nanoelectronic devices, and the latter is of interest to simulation and design of nanostructures and materials, such as applications of density functional theory. Using the proposed methodology, a very accurate prediction can be realized with a reduction in the DoF by more than 3 orders of magnitude and in the computational time by 2 orders, compared to DNS. The proposed physics-informed learning methodology is also able to offer an accurate prediction beyond the training conditions, including higher external field and larger internal potential in untrained quantum states.
Improved Knowledge Distillation for Pre-trained Language Models via Knowledge Selection
Authors: Chenglong Wang, Yi Lu, Yongyu Mu, Yimin Hu, Tong Xiao, Jingbo Zhu
Date: 2023-02-01 00:00:00
Tasks: Knowledge Distillation

Knowledge distillation addresses the problem of transferring knowledge from a teacher model to a student model. In this process, we typically have multiple types of knowledge extracted from the teacher model. The problem is to make full use of them to train the student model. Our preliminary study shows that: (1) not all of the knowledge is necessary for learning a good student model, and (2) knowledge distillation can benefit from certain knowledge at different training steps. In response to these, we propose an actor-critic approach to selecting appropriate knowledge to transfer during the process of knowledge distillation. In addition, we offer a refinement of the training algorithm to ease the computational burden. Experimental results on the GLUE datasets show that our method outperforms several strong knowledge distillation baselines significantly.
QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing
Authors: Grace Zhang, Ayush Jain, Injune Hwang, Shao-Hua Sun, Joseph J. Lim
Date: 2023-02-01 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-task reinforcement learning (MTRL) aims to learn several tasks simultaneously for better sample efficiency than learning them separately. Traditional methods achieve this by sharing parameters or relabeled data between tasks. In this work, we introduce a new framework for sharing behavioral policies across tasks, which can be used in addition to existing MTRL methods. The key idea is to improve each task's off-policy data collection by employing behaviors from other task policies. Selectively sharing helpful behaviors acquired in one task to collect training data for another task can lead to higher-quality trajectories, leading to more sample-efficient MTRL. Thus, we introduce a simple and principled framework called Q-switch mixture of policies (QMP) that selectively shares behavior between different task policies by using the task's Q-function to evaluate and select useful shareable behaviors. We theoretically analyze how QMP improves the sample efficiency of the underlying RL algorithm. Our experiments show that QMP's behavioral policy sharing provides complementary gains over many popular MTRL algorithms and outperforms alternative ways to share behaviors in various manipulation, locomotion, and navigation environments. Videos are available at https://qmp-mtrl.github.io.