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

No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
Aug 9, 2026Subhabrata Majumdar, Anand Deo, Partha Pratim Saha +1Neural Network Generalization Neural Network Robustness
Indian Institute of Management Bangalore · Independent Researcher · Indian Statistical Institute, Kolkata
Optimal Learning Under Tsybakov Noise
Aug 9, 2026Steve Hanneke, Hongao Wang, Mingyue XuNoisy-Label Learning PAC Learning
Department of Computer Science Purdue University
Constrained Learning with Universally Learnable Concept Classes
Aug 9, 2026Herlock SeyedAbolfazl Rahimi, Spyridon Pougkakiotis, Dionysis KalogeriasPrimal-Dual Optimization Reproducing Kernel Hilbert Spaces
1Yale University · 2King’s College London
Exact Rank and Convex Calibration Dimension Lower Bounds for the Multi-Label F1 Loss
Aug 9, 2026Mingyuan ZhangMulti-Label Classification Model Calibration
Independent Researcher
High-dimensional ridgeless least squares interpolation under spiked covariance structures
Aug 7, 2026Zhijun Liu, Dandan JiangDouble Descent Benign Overfitting
College of Sciences, Northeastern University, China. · School of Mathematics and Statistics, Xi’an Jiao Tong University, China.
A Rate Separation for Agnostic Direct Sums
Aug 7, 2026Mihir More, Aritra Das, Debayan GuptaPAC Learning Statistical Learning Theory
Truth Audit Labs
Multiscale Reward Hedging from Correct Demonstrations
Aug 7, 2026Pahan DewasurendraLearning from Demonstration Statistical Learning Theory
Johns Hopkins University
An Optimal Agnostic PAC Algorithm
Aug 6, 2026Markus Engelund Mathiasen, Jian Qian, Nikita ZhivotovskiyPAC Learning Statistical Learning Theory
Department of Computer Science, Aarhus University · Division of Artificial Intelligence and Data Science, School of Computing and Data Science, The University of Hong Kong · Department of Statistics, University of California, Berkeley
Optimal Rates for Learning with Monotone Adversaries
Aug 6, 2026Anay MehrotraMinimax Estimation Statistical Learning Theory
Stanford University
Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction
Aug 6, 2026Anton Conrad, Rustam Isaev, Denis Belomestny +2Conformal Prediction Statistical Learning Theory
Laboratoire de Recherche d’EPITA, 14–16 rue Voltaire, 94276 Le Kremlin-Bicêtre CEDEX, France · Faculty of Computer Science, HSE University, 11 Pokrovsky Boulevard, Moscow 109028, Russia · Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, 1 Leninskie Gory, Building 52, Moscow 119991, Russia +2
Variational Bounds for Perceptron Learning from Structured Data
Aug 5, 2026Francesco Camilli, Pierluigi Contucci, Federica Gerace +1Statistical Physics of Learning Statistical Learning Theory
Department of Mathematics, Alma Mater Studiorum – Universit`a di Bologna, Piazza di Porta San Donato 5, 40126 Bologna, Italy
The Sample Complexity of Distributionally Robust PAC Learning under Cressie--Read Divergences
Aug 5, 2026Elad Aigner-Horev, Daniel Rosenberg, Roi WeissSample Complexity Distributionally Robust Optimization
School of Computer Science and AI Ariel University 40800 Ariel, Israel
Sample Complexity of Multicalibration for Multilevel Properties
Aug 4, 2026Jiuyao Lu, Krishnakumar Balasubramanian, Aleksandr Podkopaev +1Sample Complexity Model Calibration
Department of Statistics and Data Science, The Wharton School, University of Pennsylvania · 2Amazon · Department of Statistics, University of California, Davis
To Describe or Construct Statistical Learning Models Using the Category-theoretical Language
Aug 4, 2026Congwei SongStatistical Learning Theory
Benign interpolation and Occam's razor
Aug 4, 2026Tom F. Sterkenburg, Daniel A. Herrmann, Jan-Willem RomeijnBenign Overfitting Inductive Bias
Self-Certification of Representation Adequacy: Sequential Certification at Minimum Task Loss
Aug 3, 2026Zijie HuangState Representation Learning Statistical Learning Theory
Independent Researcher
Sharp Root Anti-Concentration via Projective Incidence and Ordered Root Laws
Aug 3, 2026Zijun Wang, Yuchen Miao, Yifan Hu +1Online Convex Optimization Statistical Learning Theory
Northeastern University, China · Beijing Jiaotong University, China · Xiangtan University, China
The Label Defines the Timescale: Trait-State Limits of Temporal-Aggregate Learning
Aug 3, 2026Xizhe ZhangLatent Dynamics Modeling State Tracking
Statistical Mechanics of Learning on Product Wasserstein Manifolds
Aug 2, 2026Srinivasa Rao P Vangmayi P ReddyStatistical Physics of Learning Wasserstein Gradient Flows
Curlvee Technolabs, India; Former Scientist, C-DAC.
Active Regression for Single-Index Models with Unknown Link Functions
Aug 2, 2026Chansophea Wathanak In, Yi Li, Wai Ming Tai +1Single-Index Models Active Learning
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore · Independent Researcher · John Hopcroft Center for Computer Science, Shanghai Jiao Tong University, China
Factorized AdaBoost.MH Achieves the Same Convergence Rate as AdaBoost.MH
Aug 2, 2026Xin Zou, Jingyuan XuEnsemble Learning Multiclass Classification
The Fourth Quadrant: A Stylized View of Benign Misfitting
Aug 2, 2026Gireeja Ranade, Anant SahaiBenign Overfitting Linear Regression
University of California, Berkeley
Hierarchical Solomonoff Induction: An Unbounded Machine Learning Model
Aug 2, 2026Nathan YoungHierarchical Bayesian Modeling Statistical Learning Theory
Strong AI Lab, University of Auckland
Who Wins Where? Conformal Model Comparison for Local Superiority
Jul 31, 2026Yi Zhou, Baishi Li, Xuan Yao +1Model Selection Selective Prediction
Asian Institute of Digital Finance, National University of Singapore
Fast Rates for Swap-Agnostic Learning of Proper Losses
Jul 30, 2026Princewill OkoroaforStatistical Learning Theory Online Learning
Harvard University
The Noise Premium in Adversarial Training for Kernel Regression
Jul 30, 2026Yiling Xie, Xiaoming HuoAdversarial Training Kernel Regression
Department of Decision Analytics and Operations, City University of Hong Kong · H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology
Error Analysis of Neural-Network-Based Engression
Jul 30, 2026Juntong Chen, Zijian Guo, Xinwei ShenConditional Distribution Estimation Neural Network Approximation Theory
School of Mathematical Sciences, Xiamen University · Center for Data Science, Zhejiang University · Department of Statistics, University of Washington
Tight Sample Complexity for Low-Rank Adaptation: Matching Bounds and Rank Selection
Jul 30, 2026Arunan JSample Complexity Low-Rank Adaptation
Independent Researcher, Chennai, India.
An analysis of binary isotonic regression: degrees of freedom and implications for calibration
Jul 29, 2026Raphael Rossellini, Rina Foygel Barber, Zhimei Ren +1Probability Calibration Model Calibration
Department of Statistics, University of Chicago · Department of Statistics and Data Science, University of Pennsylvania · Department of Statistics, University of Michigan
PIKS: Universal Physics-Informed Kernel Methods
Jul 29, 2026Joachim Bona-Pellissier, Giacomo Meanti, Matteo Santacesaria +1Kernel Methods Physics-Informed ML
MaLGa Center, DIBRIS, Università degli Studi di Genova, Genoa, Italy · MaLGa Center, DIMA, Università degli Studi di Genova, Genoa, Italy · Istituto Italiano di Tecnologia, Genoa, Italy

Aug 9, 2026, cs.LGJ/K move · Enter open · S save

No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers

Subhabrata Majumdar, Anand Deo, Partha Pratim Saha, Abhik Ghosh

Indian Institute of Management Bangalore · Independent Researcher · Indian Statistical Institute, Kolkata

Neural Network Generalization Neural Network Robustness Robust Loss Functions Learning with Noisy Labels Statistical Learning Theory Robust Estimation

Open paper AskPDF arXiv

Neural network classifiers trained by cross-entropy minimization are highly sensitive to label noise and adversarial contamination. While robust alternatives offer bounded influence and resistance to corruption, their statistical foundations in the deep learning setting are insufficient due to a fundamental difficulty: neural parameterizations are non-identifiable, so the population loss minimizer is an equivalence class of parameters, not a unique point. We develop a consistency theory for robust neural classifiers based on the S-divergence family that requires no identifiability assumption. Casting training as stochastic optimization over a non-identifiable parameter space, we prove that empirical S-divergence minimizers converge to the population-optimal equivalence class under mild regularity conditions, and verify these conditions for three architecture choices. We further establish that limit points of the robust training algorithm are stationary points of the empirical objective. Experiments on vision and language benchmark datasets confirm that S-divergence training maintains clean-data accuracy while exhibiting performance competitive with existing robust methods.

Momentum

Related topics

Latest papers 395