Statistical Learning Theory

Follow this topic Ask about this topic

Momentum

34 papers in the last four weeks, up 127% on the four weeks before. 0.3% of all new papers.

Jul 13Week of Sep 28

Latest papers 395

All topics

Pessimistic Risk-Aware Policy Learning in Contextual Bandits
May 15, 2026Yilong Wan, Yuqiang Li, Xianyi WuRisk-Sensitive RL Contextual Bandits
Learning with Shallow Neural Networks on Cluster-Structured Features
May 14, 2026Elisabetta Cornacchia, Laurent MassouliéShallow Neural Networks Neural Network Generalization
INRIA, DI/ENS, PSL.
Fast Rates for Inverse Reinforcement Learning
May 14, 2026Andreas Schlaginhaufen, Maryam KamgarpourReinforcement Learning Statistical Learning Theory
Sycamore, EPFL
Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model
May 14, 2026Arie Wortsman-Zurich, Hugo Tabanelli, Yatin Dandi +2Hierarchical Representation Learning Scaling Laws
Département d’Informatique, Ecole Normale Supérieure, PSL & CNRS · Information Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)
Finite Sample Bounds for Learning with Score Matching
May 13, 2026Devin Smedira, Abhijith Jayakumar, Sidhant Misra +2Score Matching Sample Complexity
Operations Research Center, Massachusetts Institute of Technology · Theoretical division, Los Alamos National Lab
Wahkon: A Statistically Principled Deep RKHS Superposition Network
May 13, 2026Yongkai Chen, Wenxuan Zhong, Ping MaReproducing Kernel Hilbert Spaces Kernel Methods
Department of Statistics, Harvard University · Department of Statistics, University of Georgia
What is Learnable in Valiant's Theory of the Learnable?
May 13, 2026Steve Hanneke, Anay Mehrotra, Grigoris Velegkas +1PAC Learning Statistical Learning Theory
Purdue University · Stanford University · Google Research +1
Scale-Sensitive Shattering: Learnability and Evaluability at Optimal Scale
May 13, 2026Shashaank Aiyer, Yishay Mansour, Shay Moran +2Statistical Learning Theory Integral Probability Metrics
University of Maryland · Tel Aviv University and Google Research · Technion and Google Research +1
Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
May 13, 2026Yatin Dandi, Matteo Vilucchio, Luca Arnaboldi +2Hierarchical Representation Learning Neural Network Training Dynamics
Information Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)
Strategic PAC Learnability via Geometric Definability
May 13, 2026Yuval Filmus, Shay Moran, Elizaveta Nesterova +2Strategic Classification PAC Learning
Faculty of Mathematics, Technion – Israel Institute of Technology. · Faculty of Computer Science, Technion – Israel Institute of Technology. · Faculty of Data and Decision Sciences, Technion – Israel Institute of Technology. +1
Kernel-based guarantees for nonlinear parametric models in Bayesian optimization
May 13, 2026Rafael OliveiraBayesian Optimization Adaptive Sampling
The Commonwealth Scientific and Industrial Research Organisation (CSIRO) Sydney, Australia
On the Generalization of Knowledge Distillation: An Information-Theoretic View
May 13, 2026Bingying Li, Haiyun HeInformation-Theoretic Generalization Bounds Statistical Learning Theory
Internet of Things Thrust, Information Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou, China
Separating Shortcut Transition from Cross-Family OOD Failure in a Minimal Model
May 13, 2026Hongmin LiLogistic Regression OOD Generalization
School of Life Science and Technology, Institute of Science Tokyo · Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo
Learning to Decide with AI Assistance under Human-Alignment
May 12, 2026Nina Corvelo Benz, Eleni Straitouri, Manuel Gomez-RodriguezAI-Assisted Decision Making Human-AI Decision Making
§Max Planck Institute for Biochemistry, Martinsried, Germany · †Max Planck Institute for Software Systems, Kaiserslautern, Germany
High-arity Sample Compression
May 12, 2026Leonardo N. Coregliano, William OpichPAC Learning Statistical Learning Theory
Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds
May 12, 2026Yunbei Xu, Yuzhe Yuan, Ruohan ZhanStatistical Learning Theory Information-Theoretic Lower Bounds
National University of Singapore · University College London
Minimax Rates and Spectral Distillation for Tree Ensembles
May 12, 2026Binh Duc Vu, David S. WatsonModel Compression Gradient-Boosted Decision Trees
King’s College London
Learning U-Statistics with Active Inference
May 12, 2026Xiaoning Wang, Yuyang Huo, Liuhua Peng +1Active Inference Active Learning
School of Statistics and Data Sciences, LPMC, KLM-DASR and LEBPS, Nankai University, Tianjin, China · School of Mathematics and Statistics, The University of Melbourne, Melbourne, Australia
Factual recall in linear associative memories: sharp asymptotics and mechanistic insights
May 11, 2026Alessio Giorlandino, Sebastian Goldt, Antoine MaillardStatistical Learning Theory Associative Memory
1International School of Advanced Studies (SISSA), Trieste, Italy · 2INRIA Paris & DI ENS, PSL University, Paris, France
Price of Quality: Sufficient Conditions for Sparse Recovery using Mixed-Quality Data
May 11, 2026Youssef Chaabouni, David GamarnikSparse Recovery Statistical Learning Theory
Operations Research Center Massachusetts Institute of Technology Cambridge, MA 02139, USA
A Spectral Framework for Closed-Form Relative Density Estimation
May 11, 2026Francis BachDensity Ratio Estimation Spectral Methods
SIERRA · Inria - Ecole Normale Supérieure · PSL Research University
Characterizing the Generalization Error of Random Feature Regression with Arbitrary Data-Augmentation
May 11, 2026Lucas Morisset, Alain Durmus, Adrien HardyNeural Network Generalization Data Augmentation
Qube Research & Technologies · École Polytechnique
The Benefits of Temporal Correlations: SGD Learns k-Juntas from Random Walks Efficiently
May 11, 2026Elisabetta Cornacchia, Dan Mikulincer, Elchanan MosselStatistical Learning Theory Stochastic Gradient Descent
1Bocconi University · University of Washington (UW). · 3Massachusetts Institute of Technology (MIT).
On Uniform Error Bounds for Kernel Regression under Non-Gaussian Noise
May 10, 2026Johannes Teutsch, Oleksii Molodchyk, Marion Leibold +2Kernel Regression Uncertainty Quantification
Chair of Automatic Control Engineering, Department of Computer Engineering, Technical University of Munich, 80333 Munich, Germany · Institute of Control Systems, Hamburg University of Technology, 21073 Hamburg, Germany · Department of Electrical and Computer Engineering, National University of Singapore, 117583 Singapore
Online Set Learning from Precision and Recall Feedback
May 10, 2026Lee Cohen, Yishay Mansour, Shay Moran +1PAC Learning Statistical Learning Theory
1Stanford University · 2Tel Aviv University and Google Research · 3Technion and Google Research +1
Instance-Adaptive Online Multicalibration
May 10, 2026Zhiming Huang, Jamie Morgenstern, Aaron Roth +1Probability Calibration Model Calibration
Paul G. Allen School of Computer Science and Engineering, University of Washington · Department of Computer and Information Sciences, University of Pennsylvania
A Complete Characterization of Learnability for Adversarial Noisy Bandits
May 9, 2026Steve Hanneke, Kun WangMulti-Armed Bandits Statistical Learning Theory
Department of Computer Science, Purdue University
Learning Theory of Transformers: Local-to-Global Approximation via Softmax Partition of Unity
May 9, 2026Zhongjie Shi, Wenjing LiaoNeural Network Generalization Transformer
School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, United States
Learnability and Competition in High-Dimensional Multi-Component ICA
May 8, 2026Eser Ilke Genc, Samet Demir, Zafer DoganIndependent Component Analysis Unsupervised Representation Learning
MLIP Research Group, KUIS AI Center, Koç University · Department of EEE, Koç University
A Deep Risk Estimator for Known Operator Learning
May 8, 2026Andreas Maier, Md Hasan, Paulina Conrad +1Neural Network Generalization Neural Network Approximation Theory
Pattern Recognition Lab · Friedrich-Alexander-Universität Erlangen-Nürnberg · Erlangen, Germany

May 15, 2026, stat.MLJ/K move · Enter open · S save

Pessimistic Risk-Aware Policy Learning in Contextual Bandits

Yilong Wan, Yuqiang Li, Xianyi Wu

Risk-Sensitive RL Contextual Bandits Statistical Learning Theory

Open paper AskPDF arXiv

We study risk-aware offline policy learning, aiming to learn a decision rule from logged data that is optimal under general risk criteria. This problem is crucial in high-stakes domains where online interaction is infeasible and adverse outcomes must be carefully controlled. However, existing literature on offline contextual bandits either centers on expected-reward criteria or restricts risk considerations to policy evaluation instead of optimization. In this work, we propose a unified distributional framework for optimizing Lipschitz-continuous risk functionals, a broad class of risk measures encompassing mean-variance, entropic risk, and conditional value-at-risk, among others. By developing novel empirical concentration inequalities for importance sampling-based distributional estimators, our analysis derives data-dependent suboptimality bounds with an

\tilde{\mathcal{O}}(1/\sqrt{n})

rate, without relying on restrictive uniform overlap assumptions. This rate is minimax optimal and matches that of risk-neutral offline policy optimization, indicating that optimizing general Lipschitz risk criteria incurs no additional statistical cost relative to the expected-reward.

Momentum

Related topics

Latest papers 395