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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Feature Bagging Provides Stability
Jul 29, 2026Yuheng Ma, Qiang SunEnsemble Learning Algorithmic Stability
School of Statistics, East China Normal University · University of Toronto · MBZUAI
Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction
Jul 29, 2026Rahul VazeSet Prediction Conformal Prediction
Tata Institute of Fundamental Research, Mumbai
Learning Distributions from Multiple Data Providers
Jul 27, 2026Jon Kleinberg, Amin Saberi, Xizhi Tan +1Statistical Learning Theory
Cornell University · Stanford University · Google Research
Learning switched non-linear dynamical systems from a single trajectory
Jul 26, 2026Sunny G. W. Wang, Hemant TyagiRegime-Switching Dynamical Systems Nonlinear System Identification
Division of Mathematical Sciences, SPMS, NTU Singapore 637371
Generalization bounds and sample complexity for remaining useful life prediction from complete degradation trajectories
Jul 26, 2026Huy Hoang Le, Kim-Anh NguyenRUL Estimation Sample Complexity
R&D Department, PowerMore Ltd., Da Nang 550000, Vietnam · Faculty of Electrical Engineering, University of Science and Technology, The University of Danang, Da Nang 550000, Vietnam
Local Regularization Does Not Characterize Multiclass PAC Learnability
Jul 26, 2026Eric HouMulticlass Classification Classification
Learning Ergodic Dynamical Systems from a Finite Trajectory
Jul 24, 2026Oleksii Kachaiev, Silvia Villa, Lorenzo RosascoKoopman Operator Learning Markov Models
MaLGa center, DIMA, Università degli Studi di Genova, Genoa, Italy · Istituto Italiano di Tecnologia, Genoa, Italy · MaLGa center, DIBRIS, Università degli Studi di Genova, Genoa, Italy
Smart predict-then-robustly-optimize
Jul 23, 2026Aakil Caunhye, Xuefei Lu, Belen Martin-BarraganDistribution Shift Robustness Predict-Then-Optimize
Encoding orders and trees in real-valued functions
Jul 23, 2026G Conant, C TerryStatistical Learning Theory
Fisher Widths: Local Learning Geometry and Anisotropic Recovery
Jul 22, 2026Vu Khac KyInformation Geometry Sparse Recovery
Department of Mathematics, FPT University, Vietnam
Fundamental limits of distributed multiclass classification from simple binary decisions
Jul 21, 2026Ioannis Papageorgiou, Srinivas Nomula, Ayalvadi Ganesh +2Multiclass Classification Classification
School of Mathematics, University of Bristol · Department of Electrical Communication Engineering, Indian Institute of Science.
Robust Losses from Univariate Base Functions for Noisy-Label Learning
Jul 18, 2026Peng Hu, Jianwei MaNoisy-Label Learning Robust Loss Functions
School of Mathematics, Harbin Institute of Technology, Harbin, China · Institute for Artificial Intelligence and the School of Earth and Space Sciences, Peking University, Beijing, China · Institute for Artificial Intelligence and the School of Mathematics, Harbin Institute of Technology, Harbin, China
Hierarchical Domain Generalization
Jul 17, 2026Chenxiao Yang, Zhiyuan Li, Shai Ben-David +1Domain Generalization Statistical Learning Theory
Toyota Technological Institute at Chicago · University of Waterloo
Non-Asymptotic Variational Learning for Monotone Nonlinear Multiscale Elliptic Equations: Scale-Robust Primal-Dual Bounds and Strong-Form Statistical Ill-Conditioning
Jul 17, 2026Ronald KatendePrimal-Dual Optimization Neural PDE Solvers
Department of Mathematics Kabale University Kikungiri Hill, Katuna Road, Kabale, Uganda
Retraining Seeks Stable Signals
Jul 17, 2026Moritz HardtPerformative Prediction Statistical Learning Theory
Max Planck Institute for Intelligent Systems, Tübingen, and Tübingen AI Center
Publicly-Verifiable Certificates for Statistical Algorithms
Jul 17, 2026Michael Ngo, Michael P. KimStatistical Learning Theory
MIT · Research completed while at Cornell University, supported by the Bowers Undergraduate Research Experience (BURE) and the Dean Archer Undergraduate Research Program. · Cornell University
Analytical study of the optimal combination of binary classifiers based on classifiers-induced partitioning of the training set
Jul 16, 2026Jean-Marc Brossier, Olivier LafitteEnsemble Learning Binary Classification
CNRS, Univ. Grenoble Alpes, Grenoble-INP, GIPSA-lab, Grenoble, France. · Universit´e Sorbonne Paris Nord, LAGA, UMR 7539. IRL CNRS-CRM 3457. Universit´e de Montr´eal. Canada.
CASP: Learning-Augmented Offline Approximation with Verifiable Certificates and Bounded-Loss PAC Guarantees
Jul 16, 2026Haifeng Li, Mo HaiCombinatorial Optimization Learning-Augmented Algorithms
School of Information, Central University of Finance and Economics Beijing 102206, China
Random Label Prediction Heads for Studying Memorization in Deep Neural Networks
Jul 13, 2026Marlon Becker, Jonas Konrad, Luis Garcia Rodriguez +1Neural Network Generalization Neural Network Memorization
University of Münster, Germany
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
Jul 11, 2026Yushi Hirose, Hiroo Irobe, Takafumi KanamoriSemi-Supervised Learning Statistical Learning Theory
Department of Mathematical and Computing Science, Institute of Science Tokyo · 2RIKEN Center for Advanced Intelligence Project
Similarity search generalisation in contrastive learning with InfoNCE loss
Jul 10, 2026Nick WhiteleyNeural Network Generalization Contrastive Learning
School of Mathematics, University of Bristol, U.K.
Near-Optimal Learning of Gaussian Sobolev Operators
Jul 8, 2026Ben Adcock, Michael Griebel, Gregor MaierRecursive Least Squares Operator Learning
Department of Mathematics, Simon Fraser University, Burnaby BC, Canada · Institute for Numerical Simulation, University of Bonn, Bonn, Germany · Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
Fast Rates for Semi-Supervised Learning via Data-Augmentation Graph Regularization
Jul 8, 2026Adam M. ObermanData Augmentation Graph-Based Semi-Supervised Learning
Department of Mathematics and Statistics, McGill University · Mila, Quebec AI Institute · LawZero
Statistical inverse learning and $\ell^1$ -regularization
Jul 8, 2026Abhishake Rastogi, Tatiana A. Bubba, Tapio Helin +1Sparse Recovery Statistical Learning Theory
Department of Computational Engineering, LUT University, Lappeenranta, Finland. · Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy.
The Optimal Sample Complexity of Learning Autoregressive Chain-of-Thought
Jul 8, 2026Zhiyuan LiCoT Reasoning PAC Learning
Toyota Technological Institute at Chicago
Boosting with List-Decodable Codes
Jul 7, 2026Addison Prairie, Li-Yang TanEnsemble Learning Statistical Learning Theory
Stanford
Width-Robust Learnability in Mean-Field Bayesian Neural Networks
Jul 7, 2026Dmitry Vaintrob, Kaarel HänniBayesian Neural Networks Neural Network Generalization
Principles of Intelligence
On Pairwise Quantile Regression -- Statistical Guarantees and Applications
Jul 5, 2026Romain Thérézien, Stephan Clémençon, Fantin Girard +1Pairwise Comparison Quantile Regression
1LTCI, Télécom Paris, Institut Polytechnique de Paris · 2Idémia · 3Télécom Sud-Paris, Institut Polytechnique de Paris
A Unified Framework for In-Context Learning with Causal and Masked Language Models
Jul 5, 2026Chenrui Liu, Chuanlong Xie, Falong Tan +2Masked Language Modeling Autoregressive Language Modeling
Beijing Normal University at Zhuhai Zhuhai, China · Hunan University Changsha, China · Sun Yat-sen University Shenzhen, China
Statistically Meaningful Geometry and Gauge Symmetry Breaking: A Geometric Foundation for Scientific Discovery and Intelligence Emergence
Jul 3, 2026Bing Cheng, Yi-Shuai Niu, Howell Tong +1AI for Science Causal Discovery
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China · Beijing Institute of Mathematical Sciences and Applications (BIMSA), Beijing, China · Department of Statistics, London School of Economics and Political Science, London WC2A 2AE, UK +3

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Feature Bagging Provides Stability

Yuheng Ma, Qiang Sun

School of Statistics, East China Normal University · University of Toronto · MBZUAI

Ensemble Learning Algorithmic Stability Statistical Learning Theory

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We study feature bagging through the lens of algorithmic stability. Feature bagging is an ensemble strategy that aggregates base learners trained on randomly subsampled feature subsets, possibly in a data-dependent manner. We introduce feature instability (FI), the feature-axis analogue of instance instability (II), which measures sensitivity to removing a single feature. Smaller values of II or FI correspond to stronger stability, and our experiments show that FI captures generalization-relevant information complementary to II. Within this framework, we analyze feature bagging in both a parametric linear model and a model-free setting inspired by recursive feature subsampling in random forests. In both settings, we establish formal guarantees showing that feature bagging improves the relevant stability relative to its non-bagged counterpart, with larger improvements under more aggressive subsampling. We further show that a modest number of bagging rounds is sufficient to approach the infinite-bagging stability level.

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