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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On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression
Sep 22, 2026Leonardo F. Toso, James Anderson, Nirupam Gupta +1Byzantine-Robust Federated Learning Non-IID Federated Learning
Columbia University, USA · University of Copenhagen, Denmark · Sorbonne Université and Université Paris Cité, CNRS, LPSM, France
Adversarially Robust PAC Learning with Optimal VC Rates
Sep 21, 2026Steve Hanneke, Amirreza ShaeiriSample Complexity Adversarial Robustness
Department of Computer Sciences, Purdue University, USA.
Double descent is the principle of least action
Sep 16, 2026Congzhou M ShaDouble Descent Statistical Physics of Learning
Penn Medicine Doylestown Hospital, 595 W State St, Doylestown, PA 18901, United States
Fast Learning Rates for Physics-Informed Kernel Methods
Sep 16, 2026Luc Brogat-Motte, Joachim Bona-Pellissier, Giacomo Meanti +1Kernel Regression Physics-Informed ML
SAIL Unit, Istituto Italiano di Tecnologia, Genoa, Italy · MaLGa Center, DIBRIS, Università degli Studi di Genova, Genoa, Italy
How Many Labels Does Model Choice Need? Certificates and Budgets for Selective Prediction
Sep 16, 2026Tetsuji KuboyamaModel Selection Selective Prediction
Computer Centre, Gakushuin University
Sharp margin-based generalization bounds for realizable SVM
Sep 15, 2026Steve Hanneke, Aryeh KontorovichSupport Vector Machines Statistical Learning Theory
A Weighted Kernel Method for Approximation that Adapts to Learned Multivariable Structure
Sep 15, 2026John E. Darges, Laura WeidensagerKernel Regression Kernel Methods
Department of Mathematics, Emory University · Department of Mathematics, Simon Fraser University
Benign Loss Landscapes Can Coexist with Worst-Case Hardness
Sep 14, 2026Zach Furman, Stephan Wäldchen, Yangda Bei +1Tensor Networks Statistical Learning Theory
University of Melbourne · Iliad
Relatively Smart II: Tractable or Semi-Supervised Instance-Optimal Learning
Sep 11, 2026Shaddin Dughmi, Alireza F. PourClassification Semi-Supervised Learning
University of Southern California · University of Waterloo
Generalization Analysis of Distributed Kernel-based Robust Gradient Descent Algorithms
Sep 10, 2026Jun-Yi Meng, Zheng-Chu Guo, Yuan MaoKernel Regression Communication-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
Weighted Empirical Risk Minimization for Machine Learning under Long-Range Dependence: Exact Pathwise Rates and Learning-Error Geometry
Sep 9, 2026Elina MoldavskayaStatistical Learning Theory Empirical Risk Minimization
Technion–Israel Institute of Technology, Israel
A Function-Space Approach to the Statistical Mechanics of Learning Dynamics
Sep 9, 2026Yizhou Zhang, Weichen Wu, Lun Du +1Statistical Physics of Learning Representation Learning
Variational AI · The Voleon Group · Independent Researcher +1
Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks
Sep 8, 2026Xiaoyu Li, Zhizhou Sha, Jiaojiao Jiang +2Neural Network Generalization ReLU Neural Networks
University of New South Wales · University of Texas at Austin · University of Sydney
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 Bounds Stochastic Linear Dynamical Systems
CRIStAL, Centrale Lille, Universit´e de Lille, France · Department of Electronic Systems, Aalborg University, Denmark
Sharp Structure-Agnostic Minimax Risk for Partial Linear Models
Sep 7, 2026Haichen Hu, David Simchi-LeviDouble ML Semiparametric Inference
Center for Computational Science and Engineering, MIT · Department of Civil and Environmental Engineering, MIT · Institute for Data, Systems, and Society, MIT
High-Dimensional Learning Dynamics of Attention-Indexed Models
Sep 3, 2026Yizhou Xu, Margarita Sagitova, Lenka Zdeborová +1Implicit Bias Attention Mechanisms
1Information, Learning and Physics Laboratory, École Polytechnique Fédérale de Lausanne (EPFL) · 2Statistical Physics of Computation Laboratory, École Polytechnique Fédérale de Lausanne (EPFL)
Towards a Statistical Understanding of Mixture-of-Experts
Sep 3, 2026Siyuan He, Bokai Yang, Jie Hu +2Mixture of Experts Sparse 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
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 Theory Statistical Learning Theory
Occupancy-based Quantile Risk Control
Sep 2, 2026Zihao Shi, Huajun Xi, Bingyi Jing +1Conformal Risk Control Statistical Learning Theory
Southern University of Science and Technology · Mohamed bin Zayed University of Artificial Intelligence · The Chinese University of Hong Kong, Shenzhen +1
Rethinking Learnability in Offline Data-driven Optimization
Sep 1, 2026Chao Qian, Chen-Guang Wang, Rong-Xi Tan +1Black-Box Optimization Statistical Learning Theory
State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing 210023, China · School of Artificial Intelligence, Nanjing University, Nanjing 210023, China
Informative Label Missingness in Multiclass Classification Information Geometry and Excess Risk
Aug 31, 2026Fariborz Setoudehtazang, Geoffrey J. McLachlanInformation Geometry Learning with Missing Data
Dipartimento di Scienze Statistiche, Universit`a di Padova, Padova, Italy · School of Mathematics and Physics, The University of Queensland, Australia
Estimating Population-Risk Curves Along Nonconvex Gradient Flows from the Training Sample
Aug 31, 2026Mingzhi SongNonconvex Optimization Statistical Learning Theory
Department of Mathematics, The University of Hong Kong
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 Learning Statistical Learning Theory
Contact Geometry and Covariance Deficits in Volume-Sampled Least Squares
Aug 27, 2026Kihun Rhee, Hanjoon Byun, Junpyo SeoRandomized Sketching Linear Regression
Two Dimensions Govern Agnostic Multiclass Transductive Learning
Aug 26, 2026Pahan DewasurendraMulticlass Classification Transductive Learning
Johns Hopkins University
Feature Priming in Online Linear Regression: Sparse-Regret Lower Bounds and Tight Coordinatewise Rates
Aug 18, 2026Huibo Xu, Shi Fu, Qixin Zhang +1Linear Regression Statistical Learning Theory
Nanyang Technological University, Singapore
Bagging Robustly Learns VC Classes with Linear Sample Complexity
Aug 13, 2026Omar MontasserAdversarial Robustness Classification
Yale University
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 Systems Statistical Learning Theory
Universidad Nacional de Tres de Febrero. Departamento de Ciencia y Tecnología. · Universidad de Buenos Aires. Facultad de Ingeniería.
Statistical Properties of Robust Learning under Distributional Shifts
Aug 13, 2026Zhiyi Li, Xiaojie Mao, Yunbei Xu +1Distribution Shift Robustness Distributionally Robust Optimization
National University of Singapore · Tsinghua University · University College London
Logarithmic-Free Moment and Generalization Bounds for Uniformly Stable Algorithms
Aug 10, 2026Thanh Nguyen-Cung, Binh T. NguyenAlgorithmic Stability Statistical Learning Theory
VinUniversity

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On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression

Leonardo F. Toso, James Anderson, Nirupam Gupta, Rafael Pinot

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

Byzantine-Robust Federated Learning Non-IID Federated Learning Statistical Learning Theory Federated Learning

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Federated learning (FL) is intrinsically heterogeneous: honest clients may have different data-generating models. On top of that, adversarial clients can make heterogeneity even more pronounced by sharing arbitrary updates. Existing analyses typically control the interaction between statistical heterogeneity and adversarial behavior through gradient-dissimilarity conditions. However, the underlying bound is imposed a priori and may yield conservative guarantees even for least-squares regression. We instead derive the gradient heterogeneity from the statistical model of linear and nonlinear regression with fresh data samples at every round. Our bounds separate heterogeneity among the honest clients' ground-truth model parameters, finite-sample label noise, and initialization. We then demonstrate that, for any

(f,κ)

-robust aggregator with coefficient

κ= O(f/n)

, where

f

is the number of adversarial clients and

n

the total number of clients (with

f/n < 1/2

), convergence holds after an explicit sample burn-in.

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Latest papers 395