Statistical Learning Theory

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

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  1. Boosting and the Expressive Power of Simple Weak Learners via the γγ-VC Dimension

    Oct 7, 2026Arthur da Cunha, Kasper Green Larsen, Liang-Yu ZouStatistical Learning TheoryWeak-to-Strong Generalization

    Aarhus University

  2. Data Reuse in Non-Stationary Learning

    Oct 7, 2026Tomer Gafni, Garud Iyengar, Assaf ZeeviStatistical Learning TheoryDynamic Regret Minimization

    Columbia University

  3. Benign Overfitting under Heterogeneous Input Fusion

    Oct 7, 2026Houzhen Liu, Xiaobo XiaBenign OverfittingStatistical Learning Theory

    University of Science and Technology of China

  4. An Accuracy--Information Tradeoff for Loss-Difference Conditional Mutual Information

    Oct 6, 2026Hazar YuekselStatistical Learning TheoryInformation-Theoretic Generalization Bounds

  5. Exact Dynamics and Finite-Sample Trajectory Recovery of Linear Recursive Feature Machines

    Oct 6, 2026Andrew Cheng, Bobak T. Kiani, Yue M. Lu +1Statistical Learning Theory

    Harvard University · Bowdoin College · Massachusetts Institute of Technology

  6. Symmetry-Aware Feature Learning: A Polynomial Separation for Multi-Index Models

    Oct 6, 2026Jivan Waber, Vanessa Piccolo, Yatin Dandi +1Representation LearningData Augmentation

    Information, Learning and Physics Laboratory École Polytechnique Fédérale de Lausanne (EPFL) · Statistical Physics of Computation Laboratory École Polytechnique Fédérale de Lausanne (EPFL)

  7. How Many Independent Samples Does a Satellite Image Contain? Generalization Bounds for Spatially Dependent Data

    Oct 6, 2026Robin YoungSpatial GeneralizationRemote Sensing

    Department of Computer Science and Technology University of Cambridge, UK

  8. Stability of Measure-to-Measure Transformers on Sub-Gaussian Data

    Oct 6, 2026Frank Cole, Nicholas H. Nelsen, Takashi FuruyaTransformerStatistical Learning Theory

    UCLA · UT Austin · Doshisha University, RIKEN AIP

  9. Uniform Discrete Diffusion Models are Minimax Optimal for Estimating Distributions with Small Effective Support Size

    Oct 6, 2026Dongsun Yoon, Saptarshi ChakrabortyMinimax EstimationStatistical Learning Theory

    Department of Statistics, University of Michigan

  10. Is d\sqrt{d} Separation Necessary for Gradient EM to Learn Gaussian Mixtures in High Dimensions?

    Oct 6, 2026Yiran Zhang, Mo Zhou, Weihang Xu +2Gaussian Mixture ModelsExpectation-Maximization

    University of California, Berkeley · University of Washington · Amazon, Inc.

  11. Gaussian Universality and Its Breakdown in Tensor-Network Machine Learning

    Oct 5, 2026Shi-Tuan Wang, Zidu Liu, Li-Wei YuTensor NetworksGaussian Processes

    Theoretical Physics Division, Chern Institute of Mathematics and LPMC, Nankai University, Tianjin 300071, China · Max Planck Institute of Quantum Optics, 85748 Garching, Germany

  12. Classical Hardness of Learning Functions of Hamiltonians

    Oct 1, 2026Sota Hashimoto, Akinori KawachiQuantum Machine LearningStatistical Learning Theory

    Graduate School of Engineering, Mie University

  13. Estimation of the Label-Noise Transition Matrix with Performance Guarantees via Selective Classification

    Sep 30, 2026Xabier de Juan, Santiago Mazuelas, Yilun Zhu +1Noisy-Label LearningStatistical Learning Theory

    Basque Center of Applied Mathematics (BCAM) · IKERBASQUE-Basque Foundation for Science · Electrical and Computer Engineering, University of Michigan

  14. Can Domain Generalization be Guaranteed in Small-Sample Learning?

    Sep 30, 2026Hong ZhengDomain GeneralizationStatistical Learning Theory

    This work was completed during my Ph.D. period and was the result of a flash of inspiration. I decided to submit it to arXiv and leave its value to the readers.

  15. Principal Component Regression Dominates all Monotone Spectral Filters for Linear Regression

    Sep 30, 2026Juno Kim, Hengyu Fu, Peter Bartlett +2Spectral RegularizationLinear Regression

    University of California, Berkeley · Google DeepMind

  16. Asymptotic Properties of Support Vector Machines in High-Dimension, Low-Sample-Size Settings under a Spiked Model

    Sep 30, 2026Yugo NakayamaSupport Vector MachinesClassification

    Independent researcher

  17. Minimax rates for learning spectral Barron functions by deep ReLU neural networks

    Sep 30, 2026Songqiu Ma, Yunfei YangReLU Neural NetworksMinimax Estimation

    School of Mathematics (Zhuhai), Sun Yat-sen University, Zhuhai, China. · School of Mathematics (Zhuhai) and Guangdong Province Key Laboratory of Computational Science, Sun Yat-sen University, Zhuhai, China.

  18. Learn-Then-Differentiate Gradient Estimation

    Sep 30, 2026Nifei Lin, Qingkai Zhang, L. Jeff HongStatistical Learning Theory

    Research Institute for Interdisciplinary Sciences, School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai 200433, China · Department of Decision Analytics and Operations, City University of Hong Kong, Hong Kong, China · Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455

  19. How Accurate Is Accurate Enough?

    Sep 30, 2026Ningkang Peng, Qianfeng Yu, Jingyang Mao +2Statistical Learning Theory

    Nanjing Normal University · Nanjing University of Chinese Medicine

  20. Grokking through the Lens of Minimum-Norm Interpolation

    Sep 29, 2026Gil Kur, Ileana Rugina, Clémentine Carla Juliette Dominé +1Statistical Learning TheoryImplicit Regularization

    ETH Zürich · Institute of Science and Technology Austria · Harvard University

  21. Understanding Generalization Requires Universal Induction

    Sep 28, 2026Aram Ebtekar, Marcus Hutter, Danica J. SutherlandInductive BiasStatistical Learning Theory

    AIXI Labs · Google DeepMind & ANU · UBC & Amii

  22. An Active-Bottleneck Mechanism for Weak-to-Strong Generalization

    Sep 27, 2026Mohammad Zeinalpour, Amir NajafiWeak-to-Strong GeneralizationPseudo-Labeling

    Department of Computer Engineering, Sharif University of Technology

  23. A Spectral Theory of Compositional Learning

    Sep 27, 2026Hugo RydelComputational Cognitive ModelingCompositional Reasoning

    University of Manchester

  24. Benign Overfitting for General Norms and Distributions

    Sep 27, 2026Daniel Barzilai, Ohad ShamirBenign OverfittingStatistical Learning Theory

    Weizmann Institute of Science · University of Toronto & Vector Institute

  25. Geometric Identification in Predict-Then-Optimize Learning

    Sep 27, 2026Jiaxiao Xu, Changhong Mou, Keji Liu +2Predict-Then-OptimizeDecision-Focused Learning

    Shanghai University of Finance and Economics · Utah State University

  26. The Statistical Benefits of Multiple Responses for Learning from Demonstrations

    Sep 27, 2026Chandramauli Chakraborty, Cong MaLearning from DemonstrationStatistical Learning Theory

    Department of Statistics, University of Chicago

  27. Even Sharper Bounds for Transductive Learning and Its Applications

    Sep 23, 2026Yingzhen YangTransductive LearningStatistical Learning Theory

    Arizona State University, 699 S Mill RD, Tempe, AZ 85281, USA

  28. On the Sample Complexity of Active Learning with Membership Queries

    Sep 23, 2026Ganghua Wang, Shaddin DughmiActive LearningStatistical Learning Theory

    Department of Mathematics University of Arizona · Department of Computer Science University of Southern California

  29. When are bosonic Gaussian states classical to learn?

    Sep 22, 2026Senrui Chen, Antonio Anna Mele, Francesco Anna Mele +1Quantum State TomographyStatistical Learning Theory

    Institute for Quantum Information and Matter, Caltech, Pasadena, CA 91125, USA · Dahlem Center for Complex Quantum Systems, Freie Universität Berlin, 14195 Berlin, Germany

  30. Statistical Gains from Looped Estimation under Parameter Budgets

    Sep 22, 2026Xinyu Tian, Xiaotong ShenParameter EstimationMinimax Estimation

    School of Statistics, University of Minnesota

  31. On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression

    Sep 22, 2026Leonardo F. Toso, James Anderson, Nirupam Gupta +1Byzantine-Robust Federated LearningNon-IID Federated Learning

    Columbia University, USA · University of Copenhagen, Denmark · Sorbonne Université and Université Paris Cité, CNRS, LPSM, France

  32. Adversarially Robust PAC Learning with Optimal VC Rates

    Sep 21, 2026Steve Hanneke, Amirreza ShaeiriSample ComplexityAdversarial Robustness

    Department of Computer Sciences, Purdue University, USA.

  33. Double descent is the principle of least action

    Sep 16, 2026Congzhou M ShaDouble DescentStatistical Physics of Learning

    Penn Medicine Doylestown Hospital, 595 W State St, Doylestown, PA 18901, United States

  34. Fast Learning Rates for Physics-Informed Kernel Methods

    Sep 16, 2026Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti +1Kernel RegressionPhysics-Informed ML

    SAIL Unit, Istituto Italiano di Tecnologia, Genoa, Italy · MaLGa Center, DIBRIS, Università degli Studi di Genova, Genoa, Italy

  35. How Many Labels Does Model Choice Need? Certificates and Budgets for Selective Prediction

    Sep 16, 2026Tetsuji KuboyamaModel SelectionSelective Prediction

    Computer Centre, Gakushuin University

  36. Sharp margin-based generalization bounds for realizable SVM

    Sep 15, 2026Steve Hanneke, Aryeh KontorovichSupport Vector MachinesStatistical Learning Theory

  37. A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure

    Sep 15, 2026John E. Darges, Laura WeidensagerKernel RegressionKernel Methods

    Department of Mathematics, Emory University · Department of Mathematics, Simon Fraser University

  38. Benign Loss Landscapes Can Coexist with Worst-Case Hardness

    Sep 14, 2026Zach Furman, Stephan Wäldchen, Yangda Bei +1Tensor NetworksStatistical Learning Theory

    University of Melbourne · Iliad

  39. Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning

    Sep 11, 2026Shaddin Dughmi, Alireza F. PourClassificationSemi-Supervised Learning

    University of Southern California · University of Waterloo

  40. Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms

    Sep 10, 2026Jun-Yi Meng, Zheng-Chu Guo, Yuan MaoKernel RegressionCommunication-Efficient Distributed Training

    School of Mathematical Sciences, Zhejiang University, Hangzhou 310058, P. R. China · College of Informatics, Huazhong Agricultural University, Wuhan 430070, P. R. China

  41. Weighted Empirical Risk Minimization for Machine Learning under Long-Range Dependence: Exact Pathwise Rates and Learning-Error Geometry

    Sep 9, 2026Elina MoldavskayaStatistical Learning TheoryEmpirical Risk Minimization

    Technion–Israel Institute of Technology, Israel

  42. A Function-Space Approach to the Statistical Mechanics of Learning Dynamics

    Sep 9, 2026Yizhou Zhang, Weichen Wu, Lun Du +1Statistical Physics of LearningRepresentation Learning

    Variational AI · The Voleon Group · Independent Researcher +1

  43. Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks

    Sep 8, 2026Xiaoyu Li, Zhizhou Sha, Jiaojiao Jiang +2Neural Network GeneralizationReLU Neural Networks

    University of New South Wales · University of Texas at Austin · University of Sydney

  44. PAC-Bayesian Bounds for Learning Partially Observed Stochastic Linear Time-Invariant State-Space Systems with Inputs and Sub-Gaussian Noise

    Sep 8, 2026Mihaly Petreczky, Mohamad Al Ahdab, John LethPAC-Bayesian Generalization BoundsStochastic Linear Dynamical Systems

    CRIStAL, Centrale Lille, Universit´e de Lille, France · Department of Electronic Systems, Aalborg University, Denmark

  45. Sharp Structure-Agnostic Minimax Risk for Partial Linear Models

    Sep 7, 2026Haichen Hu, David Simchi-LeviDouble MLSemiparametric Inference

    Center for Computational Science and Engineering, MIT · Department of Civil and Environmental Engineering, MIT · Institute for Data, Systems, and Society, MIT

  46. High-Dimensional Learning Dynamics of Attention-Indexed Models

    Sep 3, 2026Yizhou Xu, Margarita Sagitova, Lenka Zdeborová +1Implicit BiasAttention Mechanisms

    1Information, Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · 2Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)

  47. Towards a Statistical Understanding of Mixture-of-Experts

    Sep 3, 2026Siyuan He, Bokai Yang, Jie Hu +2Mixture of ExpertsSparse Mixture-of-Experts

    Qiuzhen College, Tsinghua University, Beijing, China. · KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, China. · Yau Mathematical Sciences Center, Tsinghua University, Beijing, China. +1

  48. A Closed-Form Formula for Consistent Lipschitz Regression on Metric Spaces with Sparse Neural Network Realizations

    Sep 2, 2026Ruiyang Hong, Hrad Ghoukasian, Anastasis KratsiosNeural Network Approximation TheoryStatistical Learning Theory

  49. Occupancy-based Quantile Risk Control

    Sep 2, 2026Zihao Shi, Huajun Xi, Bingyi Jing +1Conformal Risk ControlStatistical Learning Theory

    Southern University of Science and Technology · Mohamed bin Zayed University of Artificial Intelligence · The Chinese University of Hong Kong, Shenzhen +1

  50. Rethinking Learnability in Offline Data-driven Optimization

    Sep 1, 2026Chao Qian, Chen-Guang Wang, Rong-Xi Tan +1Black-Box OptimizationStatistical Learning Theory

    State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China

  51. Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk

    Aug 31, 2026Fariborz Setoudehtazang, Geoffrey J. McLachlanInformation GeometryLearning with Missing Data

    Dipartimento di Scienze Statistiche, Universit`a di Padova, Padova, Italy · School of Mathematics and Physics, The University of Queensland, Australia

  52. Estimating Population-Risk Curves Along Nonconvex Gradient Flows from the Training Sample

    Aug 31, 2026Mingzhi SongNonconvex OptimizationStatistical Learning Theory

    Department of Mathematics, The University of Hong Kong

  53. A Borel Concept Class of VC Dimension One with a Non-PAC Consistent Learner in ZFC

    Aug 31, 2026Mateus Jesus de Arruda Campos, Gabriel Fernandes, Vinicius de Oliveira RodriguesPAC LearningStatistical Learning Theory

  54. Contact Geometry and Covariance Deficits in Volume-Sampled Least Squares

    Aug 27, 2026Kihun Rhee, Hanjoon Byun, Junpyo SeoRandomized SketchingLinear Regression

  55. Two Dimensions Govern Agnostic Multiclass Transductive Learning

    Aug 26, 2026Pahan DewasurendraMulticlass ClassificationTransductive Learning

    Johns Hopkins University

  56. Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and Tight Coordinatewise Rates

    Aug 18, 2026Huibo Xu, Shi Fu, Qixin Zhang +1Linear RegressionStatistical Learning Theory

    Nanyang Technological University, Singapore

  57. Bagging Robustly Learns VC Classes with Linear Sample Complexity

    Aug 13, 2026Omar MontasserAdversarial RobustnessClassification

    Yale University

  58. On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective

    Aug 13, 2026Nestor R. Barraza, Gabriel PenaStochastic Linear Dynamical SystemsStatistical Learning Theory

    Universidad Nacional de Tres de Febrero. Departamento de Ciencia y Tecnología. · Universidad de Buenos Aires. Facultad de Ingeniería.

  59. Statistical Properties of Robust Learning under Distributional Shifts

    Aug 13, 2026Zhiyi Li, Xiaojie Mao, Yunbei Xu +1Distribution Shift RobustnessDistributionally Robust Optimization

    National University of Singapore · Tsinghua University · University College London

  60. Logarithmic-Free Moment and Generalization Bounds for Uniformly Stable Algorithms

    Aug 10, 2026Thanh Nguyen-Cung, Binh T. NguyenAlgorithmic StabilityStatistical Learning Theory

    VinUniversity