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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Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning
May 2, 2026Richeng Zhou, Xuelin Zhang, Liyuan LiuMetric Learning Statistical Learning Theory
Networked Information Aggregation for Binary Classification
May 1, 2026MohammadHossein Bateni, Zahra Hadizadeh, MohammadTaghi Hajiaghayi +2Logistic Regression Binary Classification
Google Research, New York City, NY, USA · University of California, Irvine, CA, USA · University of Maryland, College Park, MD, USA
Man, Machine, and Mathematics
Apr 29, 2026Akshunna S. DograOptimization Convergence Analysis Statistical Learning Theory
The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI), USA · EPSRC Centre for Doctoral Training: Mathematics of Random Systems, UK · Center of Mathematical Sciences and Applications, Harvard University, USA +3
On the Learning Curves of Revenue Maximization
Apr 29, 2026Steve Hanneke, Alkis Kalavasis, Shay Moran +1Sample Complexity Statistical Learning Theory
Purdue University · Yale University · Technion +1
Near-Optimal Cryptographic Hardness of Learning With Homogeneous Halfspaces Under Gaussian Marginals
Apr 29, 2026Jizhou Huang, Brendan JubaStatistical Learning Theory
Meta · Washington Universtiy in St. Louis
Elite-Driven Support Vector Machines for Classification
Apr 28, 2026Mohammad Jafari Jozani, Bahram MoeinianfarBinary Classification Support Vector Machines
Department of Statistics, University of Manitoba, Winnipeg, Manitoba, CANADA, R3T2N2
Null Measurability at the Symmetrization Interface in VC Learning
Apr 27, 2026Dhruv GuptaPAC Learning Statistical Learning Theory
AI and Robotics Technology Park, Indian Institute of Science, Bengaluru
The Optimal Sample Complexity of Multiclass and List Learning
Apr 27, 2026Chirag PabbarajuSample Complexity Multiclass Classification
Stanford University
Generalization Bounds of Spiking Neural Networks via Rademacher Complexity
Apr 26, 2026Shao-Qun Zhang, Zhi-Hua ZhouSpiking Neural Networks Statistical Learning Theory
National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210063, China. · School of Intelligent Science and Technology, Nanjing University, Suzhou 215163, China. · School of Artificial Intelligence, Nanjing University, Nanjing 210063, China.
High-dimensional Semi-supervised Classification via the Fermat Distance
Apr 26, 2026Ruoxu Tan, Yiming Zangk-Nearest Neighbors Semi-Supervised Learning
School of Mathematical Sciences and School of Economics and Management, Tongji University · Department of Sciences, North China University of Technology
On (not) learning the Möbius function
Apr 25, 2026Alexey PozdnyakovStatistical Learning Theory
The Power of Power Law: Asymmetry Enables Compositional Reasoning
Apr 24, 2026Zixuan Wang, Xingyu Dang, Jason D. Lee +1Long-Tail Learning Compositional Reasoning
1Princeton University · University of California, Berke · 3Tsinghua University
The Sample Complexity of Multicalibration
Apr 23, 2026Natalie Collina, Jiuyao Lu, Georgy Noarov +1Probability Calibration Sample Complexity
University of Pennsylvania
There Will Be a Scientific Theory of Deep Learning
Apr 23, 2026Jamie Simon, Daniel Kunin, Alexander Atanasov +11Neural Network Training Dynamics Statistical Learning Theory
UC Berkeley and Imbue · UC Berkeley · Harvard University +6
Supervised Learning Has a Necessary Geometric Blind Spot: Theory, Consequences, and Minimal Repair
Apr 23, 2026Vishal RajputAdversarial Training Representation Geometry
1KU Leuven, Belgium.
CLT-Optimal Parameter Error Bounds for Linear System Identification
Apr 23, 2026Yichen Zhou, Stephen TuDynamical Systems Parameter Estimation
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.
Too Sharp, Too Sure: When Calibration Follows Curvature
Apr 22, 2026Alessandro Morosini, Matea Gjika, Tomaso Poggio +1Model Calibration Neural Network Training Dynamics
Massachusetts Institute of Technology, Cambridge, MA, USA.
Decentralized Machine Learning with Centralized Performance Guarantees via Gibbs Algorithms
Apr 22, 2026Yaiza Bermudez, Samir M. Perlaza, Iñaki EsnaolaDecentralized Learning Statistical Learning Theory
Centre Inria d'Université Côte d'Azur, INRIA, Sophia Antipolis, France. · Laboratoire GAATI, Université de la Polynésie française, Fa'a'ā, French Polynesia. · ECE Dept. Princeton University, Princeton, 08544 NJ, USA. +1
SMART: A Spectral Transfer Approach to Multi-Task Learning
Apr 22, 2026Boxin Zhao, Mladen Kolar, Jinchi LvMulti-Task Learning Transfer Learning
University of Chicago · University of Southern California
Ultrametric OGP - parametric RDT \emph{symmetric} binary perceptron connection
Apr 21, 2026Mihailo StojnicStatistical Learning Theory
[cs.LG]
Separating Geometry from Probability in the Analysis of Generalization
Apr 21, 2026Maxim Raginsky, Benjamin RechtStatistical Learning Theory Sensitivity Analysis
*University of Illinois, Urbana, IL 61801, USA · †University of California, Berkeley, CA 94720, USA
Revisiting Active Sequential Prediction-Powered Mean Estimation
Apr 20, 2026Maria-Eleni Sfyraki, Jun-Kun WangParameter Estimation Active Learning
University of California San Diego
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Apr 19, 2026Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2Continual Learning Replay-Based Continual Learning
University of Pennsylvania
On the Generalization Bounds of Symbolic Regression with Genetic Programming
Apr 19, 2026Masahiro Nomura, Ryoki Hamano, Isao OnoGenetic Programming Statistical Learning Theory
Institute of Science Tokyo, Yokohama, Japan · CyberAgent, Shibuya, Japan
PAC-Bayes Bounds for Gibbs Posteriors via Singular Learning Theory
Apr 19, 2026Chenyang Wang, Yun YangNeural Network Generalization PAC-Bayesian Generalization Bounds
Department of Statistics, University of Illinois Urbana-Champaign · Department of Mathematics, University of Maryland, College Park
PRIM-cipal components analysis
Apr 16, 2026Tianhao Liu, Daniel Andrés Díaz-Pachón, J. Sunil RaoUnsupervised Learning Principal Component Analysis
Division of Biostatistics, University of Miami, Miami, FL, 33136 USA · University of Minnesota, Minneapolis, MN, 55414 USA
MinShap: A Modified Shapley Value Approach for Feature Selection
Apr 16, 2026Chenghui Zheng, Garvesh RaskuttiShapley Value Attribution Feature Selection
Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706
Tight Bounds for Learning Polyhedra with a Margin
Apr 16, 2026Shyamal Patel, Santosh VempalaPAC Learning Statistical Learning Theory
Columbia University · Georgia Tech
Learning from Equivalence Queries, Revisited
Apr 6, 2026Mark Braverman, Roi Livni, Yishay Mansour +2Statistical Learning Theory
Princeton and Google · Tel-Aviv University · Tel-Aviv University and Google +2
On the Asymptotics of Self-Supervised Pre-training: Two-Stage M-Estimation and Representation Symmetry
Mar 29, 2026Mohammad Tinati, Stephen TuSelf-Supervised Pre-Training Invariant Representation Learning
Department of Electrical and Computer Engineering, University of Southern California

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Quantifying Multimodal Capabilities: Formal Generalization Guarantees in Pairwise Metric Learning

Richeng Zhou, Xuelin Zhang, Liyuan Liu

Metric Learning Statistical Learning Theory Multimodal Learning Generalization Bounds

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Multimodal learning leverages the integration of diverse data modalities to enhance performance in complex tasks. Yet, it frequently encounters incomplete or redundant modality data in real-world scenarios. This paper presents a fine-grained theoretical analysis of the generalization properties of multimodal metric learning models, addressing critical gaps in understanding the relationship between modality selection and algorithmic performance. We establish hierarchical relationships between function classes corresponding to different modality subsets and quantify the discrepancy between learned mappings and ground truth. Through rigorous analysis of pairwise complexity within the multimodal learning framework, we derive novel generalization error bounds that reveal the joint impact of modality quantity and granularity on model performance. Our theoretical findings on both upper and lower bounds demonstrate that incorporating fine-grained modality features reduces the complexity of the hypothesis space by enhancing modality complementarity. This work offers both theoretical foundations and practical implications for improving convergence rates and accuracy in multimodal learning systems.

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