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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Counterfactually Fair Regression via Optimal Transport
May 27, 2026M. Generali Lince, S. Gaucher, J-J. Vie +1Algorithmic Fairness Counterfactual Fairness
Inria, Soda team · CREST, ENSAE, IP Paris · CMAP, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris +1
On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
May 27, 2026Zhi Zhou, Ming Yang, Shi-Yu Tian +3Test-Time Adaptation Distribution Shift
State Key Laboratory of Novel Software Technology, Nanjing University, China · School of Artificial Intelligence, Nanjing University, China · School of Intelligence Science and Technology, Nanjing University, China
Proper Agnostic Learning of Functions of Halfspaces under Gaussian Marginals
May 26, 2026Sergei Tikhonov, Arsen VasilyanBoolean Function Learning Statistical Learning Theory
UT Austin
CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk
May 26, 2026Tianxing Mei, Yingying Fan, Mingming Leng +1Ensemble Learning Statistical Learning Theory
Faculty of Business, Lingnan University · Data Sciences and Operations Department, University of Southern California
A PAC-Bayesian View of Generalisation for Physics-Informed Machine Learning
May 25, 2026Thien V. Nguyen, Amaury Habrard, Benjamin GuedjPAC-Bayesian Generalization Bounds Physics-Informed ML
Université Jean Monnet Saint-Étienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Étienne, France · Université Jean Monnet Saint-Étienne, CNRS, Institut d’Optique Graduate School, Laboratoire Hubert Curien UMR 5516, Inria, F-42023, Saint-Étienne, France · Institut Universitaire de France +1
Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding
May 25, 2026Rustem Takhanov, Zhenisbek AssylbekovKernel Ridge Regression Kernel Regression
Department of Mathematics, Nazarbayev University, Astana, Kazakhstan · Nazarbayev University Research Administration, Astana, Kazakhstan · Purdue University Fort Wayne, Indiana, USA.
Minimax Limits of k-Fold Cross-Validation via Majority
May 25, 2026Ido Nachum, Rüdiger Urbanke, Thomas WeinbergerMinimax Estimation Statistical Learning Theory
University of Haifa, Haifa, Israel. · EPFL, Lausanne, Switzerland.
PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting
May 25, 2026Steve Hanneke, Qinglin Meng, Shay Moran +1Multiclass Classification Classification
Department of Computer Science Purdue University West Lafayette, IN, USA · Faculties of Mathematics, Computer Science, and Data and Decision Sciences Technion – Israel Institute of Technology and Google Research Haifa, Israel
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
May 25, 2026Nil Ayday, Mahalakshmi Sabanayagam, Debarghya GhoshdastidarNeural Network Generalization Graph Neural Networks
Technical University of Munich, School of Computation, Information and Technology, Boltzmannstr. 3, Garching, 85748, Germany. · Australian Institute for Machine Learning, Adelaide University,2026 Adelaide, SA 5000, Australia.
Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning
May 24, 2026Ziheng Cheng, Yixiao Huang, Hanlin Zhu +5Diffusion Policy Semi-Supervised Learning
University of California, Berkeley.
Estimating Mixture Distributions via Stochastic Mirror Descent
May 24, 2026Mohammadreza Ahmadypour, Tara Javidi, Farinaz KoushanfarStochastic Optimization Statistical Learning Theory
Department of Electrical and Computer Engineering University of California San Diego, San Diego, CA, USA
On the Sample Complexity of Robust Binary Hypothesis Testing
May 23, 2026Shankar Vallinayagam, Ankit Pensia, Varun JogSample Complexity Data Contamination
Department of Pure Mathematics and Mathematical Statistics, University of Cambridge · Department of Statistics, Carnegie Mellon University
Multicalibration Boosting: Theory, Convergence, and Transferability
May 23, 2026Hanxuan Ye, Hongzhe LiPost-Hoc Calibration Model Calibration
Department of Biostatistics and Epidemiology University of Pennsylvania Philadelphia, PA 19104-6021, USA · Department of Biostatistics and Epidemiology University of Pennsylvania2026 Philadelphia, PA 19104-6021, USA
Optimal Dimension-Free Sampling for Regularized Classification
May 22, 2026Meysam Alishahi, Alexander Munteanu, Simon Omlor +1Sample Complexity Statistical Learning Theory
University of Utah, USA · TU Dortmund, Germany
Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning
May 21, 2026Adrien Weihs, Hayden SchaefferNeural Network Approximation Theory Operator Learning
Department of Mathematics, University of California Los Angeles, Los Angeles, CA 90095, USA.
When Stronger Triggers Backfire: A High-Dimensional Theory of Backdoor Attacks
May 21, 2026Donald Flynn, Hadas Yaron Goldhirsh, Jonathan P. Keating +1Backdoor Attacks Statistical Learning Theory
1Mathematical Institute, University of Oxford · School of Mathematical Science, Tel Aviv University · School of Mathematical Science and Computer Science, Tel Aviv University
Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals
May 20, 2026Ilias Diakonikolas, Giannis Iakovidis, Mingchen MaMulticlass Classification Classification
University of Wisconsin-Madison
A Rigorous, Tractable Measure of Model Complexity
May 20, 2026Oskar Allerbo, Thomas B. SchönStatistical Learning Theory
Department of Mathematics KTH Royal Institute of Technology · Department of Information Technology Uppsala University
Sample Complexity of Transfer Learning: An Optimal Transport Approach
May 19, 2026Haoyang Cao, Xin Guo, Wenpin Tang +1Sample Complexity Transfer Learning
Department of Applied Mathematics and Statistics, John Hopkins University, Baltimore, MD, USA · Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA, USA · Department of Industrial Engineering and Operations Research, Columbia University, New York, NY, USA +1
Contradiction Graphs Determine VC Dimension
May 19, 2026Jesse Campbell, Daniel Ibaibarriaga, Lev ReyzinStatistical Learning Theory
Department of Mathematics, Statistics, & Computer Science University of Illinois Chicago
When Does Model Collapse Occur in Structured Interactive Learning?
May 19, 2026Yuchen Wu, Kangjie Zhou, Weijie SuModel Collapse Generative Modeling
School of Operations Research and Information Engineering, Cornell University · Department of Statistics, Columbia University · Department of Statistics and Data Science, University of Pennsylvania
Less Data, Faster Training: repeating smaller datasets speeds up learning via sampling biases
May 19, 2026Jingwen Liu, Ezra Edelman, Surbhi Goel +1Deep Learning Optimization Statistical Learning Theory
Columbia University · University of Pennsylvania · Kempner Institute, Harvard University
Multi-Head Attention as Ensemble Nadaraya-Watson Estimation: Variance Reduction, Decorrelation, and Optimal Head Diversity
May 18, 2026Ernest FokouéSelf-Attention Ensemble Learning
School of Mathematics and Statistics, College of Science Rochester Institute of Technology Rochester, New York 14623, USA
LoRA vs. Full Fine-Tuning: A Theoretical Perspective
May 18, 2026Ali Zindari, Rotem Mulayoff, Sebastian U. StichFine-Tuning Low-Rank Adaptation
Universität des Saarlandes · CISPA Helmholtz Center for Information Security
Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
May 18, 2026Arnab Auddy, Xiangni Peng, Subhadeep PaulFederated Learning Aggregation Communication-Efficient Distributed Training
Department of Statistics · The Ohio State University
Generative Adversarial Learning from Deterministic Processes
May 18, 2026Joris C. Kühl, Hanno GottschalkDynamical Systems Statistical Learning Theory
Institute of Mathematics, Technical University of Berlin, Germany
Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models
May 18, 2026Radu Lecoiu, Debarghya Mukherjee, Pragya SurSelf-Distillation Spectral Methods
Department of Statistics, Harvard University · Department of Mathematics & Statistics, Boston University
Multi-task Linear Regression without Eigenvalue Lower Bounds: Adaptivity, Robustness, and Safety
May 16, 2026Seok-Jin KimRobust Regression Multi-Task Learning
Columbia IEOR
Statistical Unlearning of Distributions: A Hypothesis Testing Approach
May 15, 2026Aaradhya Pandey, Sanjeev KulkarniStatistical Learning Theory Machine Unlearning
Princeton University
The Privacy Price of Tail-Risk Learning: Effective Tail Sample Size in Differentially Private CVaR Optimization
May 15, 2026El Mustapha MansouriStochastic Optimization Differential Privacy
School of Engineering, Institute of Science Tokyo, Yokohama, Kanagawa 226-8501, Japan

May 27, 2026, stat.MLJ/K move · Enter open · S save

Counterfactually Fair Regression via Optimal Transport

M. Generali Lince, S. Gaucher, J-J. Vie, P. Loiseau

Inria, Soda team · CREST, ENSAE, IP Paris · CMAP, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris+1

Algorithmic Fairness Counterfactual Fairness Statistical Learning Theory

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We consider the problem of learning a counterfactually fair regressor. We adopt a causal uncertainty view in which counterfactual fairness is defined with resampled noise. We focus on obtaining theoretical fairness guarantees for a new post-processing estimator. We begin by showing that counterfactual fairness is equivalent to satisfying demographic parity conditional on the latent variable. This allows us to provide a closed-form expression of the optimal fair regressor via a barycentric quantile map. In order to handle continuous latent variables, we propose a discretized post-processing method. Then, under mild regularity assumptions, we prove high-probability finite-sample fairness guarantees for our estimator, providing an unfairness decay at rate

\tilde O(n^{-1/3})

, and establishing a matching risk bound of order

\tilde O(n^{-1/3})

. We provide a matching lower bound on the excess risk of almost fair predictions. Finally, we extend our results to the setting of relaxed counterfactual fairness. We validate our approach on real-world and synthetic data.

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Latest papers 395