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