yusuke1997/dblp-id-title huggingface dataset:
Robust and Efficient Computation of Eigenvectors in a Generalized Spectral Method for Constrained Clustering
The Learning Curve Method Applied to Clustering
A Doubly Robust Approach to Sparse Reinforcement Learning
Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning
On the Convergence of SARAH and Beyond
Faster Projection-Free Augmented Lagrangian Methods via Weak Proximal Oracle
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization
Transformers are Provably Optimal In-context Estimators for Wireless Communications
Clustering multilayer graphs with missing nodes
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders
Automatic Differentiation of Some First-Order Methods in Parametric Optimization
International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach
Tackling the XAI Disagreement Problem with Regional Explanations
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Almost Optimal Universal Lower Bound for Learning Causal DAGs with Atomic Interventions
On the complexity of the optimal transport problem with graph-structured cost
Hierarchical Clustering in General Metric Spaces using Approximate Nearest Neighbors
Active Boundary Annotation using Random MAP Perturbations
Adaptive Online Kernel Sampling for Vertex Classification
Revisiting Adversarial Risk
Adaptive MCMC via Combining Local Samplers
Feature subset selection for the multinomial logit model via mixed-integer optimization
Reward Maximization for Pure Exploration: Minimax Optimal Good Arm Identification for Nonparametric Multi-Armed Bandits
(Bandit) Convex Optimization with Biased Noisy Gradient Oracles
The 24th International Conference on Artificial Intelligence and Statistics, AISTATS 2021, April 13-15, 2021, Virtual Event
On the Convergence of Stochastic Extragradient for Bilinear Games using Restarted Iteration Averaging
New Resistance Distances with Global Information on Large Graphs
Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization
Hierarchically-partitioned Gaussian Process Approximation
A Diversity-aware Model for Majority Vote Ensemble Accuracy
An Optimal Algorithm for Adversarial Bandits with Arbitrary Delays
Stochastic Difference of Convex Algorithm and its Application to Training Deep Boltzmann Machines
Graph Community Detection from Coarse Measurements: Recovery Conditions for the Coarsened Weighted Stochastic Block Model
Faithful graphical representations of local independence
Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorship
A Unified Framework for Discovering Discrete Symmetries
Symmetric Equilibrium Learning of VAEs
Risk-Sensitive Generative Adversarial Imitation Learning
Convex Analysis of the Mean Field Langevin Dynamics
Thompson Sampling for Linearly Constrained Bandits
Bandit Online Learning with Unknown Delays
REPID: Regional Effect Plots with implicit Interaction Detection
Implications of sparsity and high triangle density for graph representation learning
Decision-Point Guided Safe Policy Improvement
Crowdclustering with Partition Labels
SwAMP: Swapped Assignment of Multi-Modal Pairs for Cross-Modal Retrieval
Approximate Bayesian Class-Conditional Models under Continuous Representation Shift
Duel-based Deep Learning system for solving IQ tests
Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems
Truly Batch Model-Free Inverse Reinforcement Learning about Multiple Intentions
Localization and Adaptation in Online Learning
Transfer of information between system and evidence models
Federated Learning for Data Streams
Accelerating ABC methods using Gaussian processes
Regularized ERM on random subspaces
Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fréchet Median
Minimal Expected Regret in Linear Quadratic Control
Federated Learning with Buffered Asynchronous Aggregation
Iterative Supervised Principal Components
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime
Orthogonal Gradient Descent for Continual Learning
Corruption-robust Offline Reinforcement Learning
Robust Forward Algorithms via PAC-Bayes and Laplace Distributions
Complementary Sum Sampling for Likelihood Approximation in Large Scale Classification
A New Notion of Individually Fair Clustering: α-Equitable k-Center
A Comparison of Decision Trees, Influence Diagrams and Valuation Networks for Asymmetric Decision Problems
New User Event Prediction Through the Lens of Causal Inference
Blessing of Class Diversity in Pre-training
Estimation of Large Zipfian Distributions with Sort and Snap
Nonlinear Dimensionality Reduction by Semidefinite Programming and Kernel Matrix Factorization
Learning Treatment Effects from Observational and Experimental Data
Control Representation in an EDA Assistant
Active Cost-aware Labeling of Streaming Data
Products of Hidden Markov Models
No-Regret Bayesian Optimization with Stochastic Observation Failures
Fair learning with Wasserstein barycenters for non-decomposable performance measures
Taming heavy-tailed features by shrinkage
Experimental Design for Regret Minimization in Linear Bandits
Principal Component Regression with Semirandom Observations via Matrix Completion
Robust Gradient Descent for Phase Retrieval
Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions
Density of States Estimation for Out of Distribution Detection
Multiple-policy High-confidence Policy Evaluation
Towards Memory-Friendly Deterministic Incremental Gradient Method
Stateful Offline Contextual Policy Evaluation and Learning
Learning Possibilistic Networks from Data
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes
Riemannian stochastic quasi-Newton algorithm with variance reduction and its convergence analysis
Offline Primal-Dual Reinforcement Learning for Linear MDPs
Denoising and change point localisation in piecewise-constant high-dimensional regression coefficients
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate
Transport Reversible Jump Proposals
Scalable Algorithms for Individual Preference Stable Clustering
Jointly Informative Feature Selection
Robustness of classifiers to uniform $\ell_p$ and Gaussian noise
Near-Optimal Differentially Private Reinforcement Learning
Permutation Invariant Graph Generation via Score-Based Generative Modeling
A Characterization of Mean Squared Error for Estimator with Bagging
How to Find Big-Oh in Your Data Set (and How Not To)
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
Near-Optimal Algorithm for Non-Stationary Kernelized Bandits
Smoothly Giving up: Robustness for Simple Models
Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity
An Exact Probability Metric for Decision Tree Splitting
Stochastic Rank-1 Bandits
The LORACs Prior for VAEs: Letting the Trees Speak for the Data
A New Class of Private Chi-Square Hypothesis Tests
Active Community Detection with Maximal Expected Model Change
A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models
Precision Matrix Estimation with Noisy and Missing Data
An Analysis of LIME for Text Data
Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks
Associative Convolutional Layers
The cost of local and global fairness in Federated Learning
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering
Measure Transport with Kernel Stein Discrepancy
Scalable Variational Bayesian Matrix Factorization with Side Information
Energy-consistent Neural Operators for Hamiltonian and Dissipative Partial Differential Equations
Revisiting Fair-PAC Learning and the Axioms of Cardinal Welfare
Learning Optimal Interventions
The Pivoting Framework: Frank-Wolfe Algorithms with Active Set Size Control
Dynamic Positional Trees for Structural Image Analysis
Bayes-Optimal Effort Allocation in Crowdsourcing: Bounds and Index Policies
Imposing Fairness Constraints in Synthetic Data Generation
On how complexity affects the stability of a predictor
Fast Adaptation with Linearized Neural Networks
Stable ResNet
On the Statistical Comparison of Inductive Learning Methods
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods
Unsupervised Ensemble Learning with Dependent Classifiers
Best Arm Identification with Resource Constraints
Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape
High Dimensional Inference in Partially Linear Models
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests
Improved Bound on Generalization Error of Compressed KNN Estimator
Graph DNA: Deep Neighborhood Aware Graph Encoding for Collaborative Filtering
A Further Comparison of Simplification Methods for Decision-Tree Induction
The Base Measure Problem and its Solution
Adaptive Sampling for Fast Constrained Maximization of Submodular Functions
Dropout as a Regularizer of Interaction Effects
Heterogeneous Domain Adaptation for Multiple Classes
Labeled Graph Clustering via Projected Gradient Descent
Testing exchangeability by pairwise betting
ConvNets with Smooth Adaptive Activation Functions for Regression
Extragradient Method: O(1/K) Last-Iterate Convergence for Monotone Variational Inequalities and Connections With Cocoercivity
Discrete Distribution Estimation under User-level Local Differential Privacy
Defending against Whitebox Adversarial Attacks via Randomized Discretization
Unbiased Implicit Variational Inference
Profile Likelihood in Directed Graphical Models from BUGS Output
An Algorithm for Bayesian Network Construction from Data
Continuum-Armed Bandits: A Function Space Perspective
MAP Inference for Customized Determinantal Point Processes via Maximum Inner Product Search
A Primal-Dual Solver for Large-Scale Tracking-by-Assignment
Functional Gradient Boosting for Learning Residual-like Networks with Statistical Guarantees
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
Fast column generation for atomic norm regularization
Compressed Least Squares Regression revisited
On Universal Portfolios with Continuous Side Information
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
Multi-Fidelity High-Order Gaussian Processes for Physical Simulation
Gradient Descent in RKHS with Importance Labeling
Identification and Estimation of "Causes of Effects" using Covariate-Mediator Information
Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations
High-dimensional Inference via Lipschitz Sparsity-Yielding Regularizers
Unconditionally Calibrated Priors for Beta Mixture Density Networks
Bridging the gap between regret minimization and best arm identification, with application to A/B tests
Efficient Non-Parametric Function Induction in Semi-Supervised Learning
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression
Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks
Variational Adversarial Training Towards Policies with Improved Robustness
Understanding the Effect of GCN Convolutions in Regression Tasks
Private Sequential Hypothesis Testing for Statisticians: Privacy, Error Rates, and Sample Size
Density-Dependent Group Testing
One-pass Stochastic Gradient Descent in overparametrized two-layer neural networks
Score matching for bridges without learning time-reversals
Kernel Exponential Family Estimation via Doubly Dual Embedding
Hyperparameters for Soft Bayesian Model Selection
Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty
SAN: Stochastic Average Newton Algorithm for Minimizing Finite Sums
Bayesian Reinforcement Learning via Deep, Sparse Sampling
Evidential Uncertainty Probes for Graph Neural Networks
Stochastic Weight Sharing for Bayesian Neural Networks
PAC Top-k Identification under SST in Limited Rounds
Bayesian Nonparametric Poisson-Process Allocation for Time-Sequence Modeling
A Differential Inclusion Approach for Learning Heterogeneous Sparsity in Neuroimaging Analysis
Wasserstein Distributional Learning via Majorization-Minimization
Linking Micro Event History to Macro Prediction in Point Process Models
Causal Bayesian Optimization
Near Optimal Algorithms for Hard Submodular Programs with Discounted Cooperative Costs
Deep Active Learning: Unified and Principled Method for Query and Training
Adversarial Robustness Guarantees for Classification with Gaussian Processes
On the Dynamics of Gradient Descent for Autoencoders
Random projection design for scalable implicit smoothing of randomly observed stochastic processes
Learning with Complex Loss Functions and Constraints
A Variational Information Bottleneck Approach to Multi-Omics Data Integration
CADA: Communication-Adaptive Distributed Adam
Parametric Programming Approach for More Powerful and General Lasso Selective Inference
The Security of Latent Dirichlet Allocation
The Uniformly Rotated Mondrian Kernel
Fixed Support Tree-Sliced Wasserstein Barycenter
Sum-max Submodular Bandits
Regression as Classification: Influence of Task Formulation on Neural Network Features
Variance Minimization in the Wasserstein Space for Invariant Causal Prediction
Revisiting Online Learning Approach to Inverse Linear Optimization: A Fenchel-Young Loss Perspective and Gap-Dependent Regret Analysis
Fundamental limits for rank-one matrix estimation with groupwise heteroskedasticity
Decentralized Multi-player Multi-armed Bandits with No Collision Information
K2-ABC: Approximate Bayesian Computation with Kernel Embeddings
Minimum-Entropy Coupling Approximation Guarantees Beyond the Majorization Barrier
A PTAS for the Bayesian Thresholding Bandit Problem
Enhancing Distributional Stability among Sub-populations
Nonparametric Bayesian sparse graph linear dynamical systems
Towards building a Crowd-Sourced Sky Map
Prior-Dependent Allocations for Bayesian Fixed-Budget Best-Arm Identification in Structured Bandits
Learning Personalized Item-to-Item Recommendation Metric via Implicit Feedback
Identifiability of Product of Experts Models
Minimax Gaussian Classification & Clustering
Boundary-Aware Uncertainty for Feature Attribution Explainers
Efficient Weight Learning in High-Dimensional Untied MLNs
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms
Dissimilarity Bandits
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Revisiting Weighted Strategy for Non-stationary Parametric Bandits
Tensor vs. Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Deep Neyman-Scott Processes
Implicit Regularization via Neural Feature Alignment
Discriminator Guidance for Autoregressive Diffusion Models
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Bayesian Multi-Scale Optimistic Optimization
Reducing Crowdsourcing to Graphon Estimation, Statistically
Function-Space MCMC for Bayesian Wide Neural Networks
An Impossibility Theorem for Node Embedding
PAC-Bayesian Collective Stability
Top Feasible Arm Identification
Sampling From Multiscale Densities With Delayed Rejection Generalized Hamiltonian Monte Carlo
Distill n' Explain: explaining graph neural networks using simple surrogates
Privacy Amplification by Decentralization
Scalable Spectral Clustering with Group Fairness Constraints
A Rule for Gradient Estimator Selection, with an Application to Variational Inference
Sparse Linear Isotonic Models
Localizing Changes in High-Dimensional Regression Models
Online Convex Optimization with Perturbed Constraints: Optimal Rates against Stronger Benchmarks
Primal-Dual Stochastic Mirror Descent for MDPs
Detection of Planted Solutions for Flat Satisfiability Problems
Performative Prediction with Neural Networks
Contextual Online False Discovery Rate Control
Learning Invariant Representations with Kernel Warping
Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting
Momentum in Reinforcement Learning
Tracking Regret Bounds for Online Submodular Optimization
Latent Maximum Entropy Approach for Semantic N-gram Language Modeling
Approximate Function Evaluation via Multi-Armed Bandits
Online Linearized LASSO
Stochastic Gradient Descent on Separable Data: Exact Convergence with a Fixed Learning Rate
Modeling simple structures and geometry for better stochastic optimization algorithms
DNNLasso: Scalable Graph Learning for Matrix-Variate Data
Reconstructing Training Data from Model Gradient, Provably
Deep anytime-valid hypothesis testing
Recommendation on a Budget: Column Space Recovery from Partially Observed Entries with Random or Active Sampling
Multi-Agent Credit Assignment with Pretrained Language Models
Robust Higher Order Statistics
RetroDiff: Retrosynthesis as Multi-stage Distribution Interpolation
Learning Priors for Invariance
On the Limitations of the Elo, Real-World Games are Transitive, not Additive
Covariate-informed Representation Learning to Prevent Posterior Collapse of iVAE
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization
Generalized Criterion for Identifiability of Additive Noise Models Using Majorization
Data-Driven Upper Confidence Bounds with Near-Optimal Regret for Heavy-Tailed Bandits
Does Invariant Risk Minimization Capture Invariance?
Statistical and computational thresholds for the planted k-densest sub-hypergraph problem
Distributionally Robust Formulation and Model Selection for the Graphical Lasso
Learning Sparse Codes with Entropy-Based ELBOs
On the Value of Prior in Online Learning to Rank
Learning Influence-Receptivity Network Structure with Guarantee
Why Steiner-tree type algorithms work for community detection
Resilient Constrained Reinforcement Learning
Explaining ViTs Using Information Flow
On Retrieval Properties of Samples of Large Collections
Spectral Pruning for Recurrent Neural Networks
Towards Cost Sensitive Decision Making
Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation
Functional Graphical Models: Structure Enables Offline Data-Driven Optimization
Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees
Matérn Gaussian Processes on Graphs
Linearly Constrained Gaussian Processes with Boundary Conditions
Improving Adaptive Conformal Prediction Using Self-Supervised Learning
Second-Order Sensitivity Analysis for Bilevel Optimization
Fractional moment-preserving initialization schemes for training deep neural networks
Reward Learning as Doubly Nonparametric Bandits: Optimal Design and Scaling Laws
Testing regression models with fewer regressors
Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression
Convex Block-sparse Linear Regression with Expanders - Provably
A Fast and Reliable Policy Improvement Algorithm
Pairwise Supervision Can Provably Elicit a Decision Boundary
Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning
Approximate Message Passing with Spectral Initialization for Generalized Linear Models
Adaptive Gaussian Copula ABC
Minimax Optimal Regression over Sobolev Spaces via Laplacian Regularization on Neighborhood Graphs
Implicit Diffusion: Efficient optimization through stochastic sampling
Variational Continual Proxy-Anchor for Deep Metric Learning
Training Implicit Generative Models via an Invariant Statistical Loss
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
Orthogonal Estimation of Wasserstein Distances
Message Length as an Effective Ockham's Razor in Decision Tree Induction
Feature relevance quantification in explainable AI: A causal problem
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms
Spectrum Extraction and Clipping for Implicitly Linear Layers
Gaussian Processes for Bayesian hypothesis tests on regression functions
Catalyst Acceleration of Error Compensated Methods Leads to Better Communication Complexity
Solving Multi-Arm Bandit Using a Few Bits of Communication
Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum
Fast and Accurate Estimation of Low-Rank Matrices from Noisy Measurements via Preconditioned Non-Convex Gradient Descent
Bayesian experimental design using regularized determinantal point processes
Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Series
Sparse Bayesian optimization
Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning
Offline RL via Feature-Occupancy Gradient Ascent
Federated Asymptotics: a model to compare federated learning algorithms
Frank-Wolfe Algorithms for Saddle Point Problems
Gradient Boosting on Stochastic Data Streams
Adversarial Random Forests for Density Estimation and Generative Modeling
Robustness Guarantees for Density Clustering
Global Convergence of Non-Convex Gradient Descent for Computing Matrix Squareroot
Performative Prediction on Games and Mechanism Design
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections
Structural Expectation Propagation (SEP): Bayesian structure learning for networks with latent variables
Central Limit Theorems for Conditional Markov Chains
Multi-armed bandits with guaranteed revenue per arm
One-Sketch-for-All: Non-linear Random Features from Compressed Linear Measurements
On the Implicit Geometry of Cross-Entropy Parameterizations for Label-Imbalanced Data
Parallelised Bayesian Optimisation via Thompson Sampling
On the Privacy Properties of GAN-generated Samples
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models
A Geometric Algorithm for Scalable Multiple Kernel Learning
Federated Causal Inference: Multi-Study ATE Estimation beyond Meta-Analysis
Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets
Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences
Stable behaviour of infinitely wide deep neural networks
Selective Sampling with Drift
Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks
Inferring Block Structure of Graphical Models in Exponential Families
The Galerkin method beats Graph-Based Approaches for Spectral Algorithms
The Nonparametric Kernel Bayes Smoother
Personalized and Private Peer-to-Peer Machine Learning
Exact Learning of Bounded Tree-width Bayesian Networks
Robust Optimisation Monte Carlo
Integer Programming Based Methods and Heuristics for Causal Graph Learning
Rep the Set: Neural Networks for Learning Set Representations
The Multiple Instance Learning Gaussian Process Probit Model
Completing the Picture: Randomized Smoothing Suffers from the Curse of Dimensionality for a Large Family of Distributions
Safe Active Learning for Multi-Output Gaussian Processes
Fast Convergence of Online Pairwise Learning Algorithms
A Non-asymptotic Approach to Best-Arm Identification for Gaussian Bandits
Variational Autoencoders: A Harmonic Perspective
Epistemic Uncertainty and Excess Risk in Variational Inference
On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning
Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision
Negative Momentum for Improved Game Dynamics
HS 2 : Active learning over hypergraphs with pointwise and pairwise queries
Iterative Alignment Flows
Relating Piecewise Linear Kolmogorov Arnold Networks to ReLU Networks
ASkewSGD : An Annealed interval-constrained Optimisation method to train Quantized Neural Networks
Sample Efficiency of Data Augmentation Consistency Regularization
Learning Temporal Point Processes with Intermittent Observations