PAC-Bayesian Generalization Bounds

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3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems
Sep 21, 2026Chenfeng Huang, George MichailidisPAC-Bayesian Generalization Bounds Meta-Learning
Department of Statistics & Data Science University of California, Los Angeles
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
Sep 15, 2026Julien Bastian, Benjamin Leblanc, Pascal Germain +4PAC-Bayesian Generalization Bounds Ensemble Learning
LabHC · Universit´e Jean Monnet Saint-´Etienne, CNRS, Institut d Optique Graduate School, Laboratoire Hubert Curien UMR 5516, F-42023, Saint-Etienne, France · LabHC, UJM, MALICE +6
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
PAC-Bayes Beyond Parameter Space: Behavioral Equivalence, Z-Information, and Exact Complexity Decomposition
Aug 11, 2026Vasant G. Honavar, Satish Kumar Keshri, Neil Ashtekar +1PAC-Bayesian Generalization Bounds
Artificial Intelligence Research Laboratory Department of Informatics and Intelligent Systems The Pennsylvania State University · Artificial Intelligence Research Laboratory Department of Computer Science and Engineering The Pennsylvania State University
PAC-DP: PAC-Bayesian Diffusion Policy Learning
Jul 27, 2026Mohammad Hasan Yeganegi, Dian Yu, Andrea Del Prete +2PAC-Bayesian Generalization Bounds Robot Policy Learning
Department of Industrial Engineering, University of Trento, Italy. · Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich (TUM), Germany.
PAC--Bayes Bounds on Quotient Parameter Spaces: Geometry-induced Implicit-Bias Priors
Jul 20, 2026Nicola Aladrah, Fabio AnselmiPAC-Bayesian Generalization Bounds
Department of Mathematics, Informatics and Geoscience, University of Trieste, Via Valerio 12/1, 34127 Trieste, Italy · 2McGovern Institute, MIT, Main Street, Cambridge, MA 02139, USA
Non-vacuous Generalization Bounds for Reinforcement Learning with Verifiable Rewards
Jul 16, 2026Yuxuan Zhu, Rohan Alur, Daniel KangPAC-Bayesian Generalization Bounds Reinforcement Learning with Verifiable Rewards
UIUC · MIT · Bridgewater AIA Labs
Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
Jul 7, 2026Jian Xu, Delu Zeng, John Paisley +1PAC-Bayesian Generalization Bounds Quantum Machine Learning
1RIKEN iTHEMS · 2RIKEN AIP · 3South China University of Technology +1
PAC-Bayesian Certificates for Quadratic Closed-Loop Control
Jun 26, 2026Domagoj HercegPAC-Bayesian Generalization Bounds Data-Driven Control
Department of Mechanical Engineering Eindhoven University of Technology The Netherlands
VeriBound: PAC-Bayesian Generalization Bounds for Process Reward Models Trained with Formal Verification Tools
Jun 17, 2026Amirul Rahman, Mohammed Sabih AlsharariPAC-Bayesian Generalization Bounds Process Reward Models
University of Malaya
Smoothness-Based Derandomization of PAC-Bayes Bounds
Jun 17, 2026Alexandre Lemire Paquin, Brahim Chaib-Draa, Philippe GiguèreNeural Network Generalization PAC-Bayesian Generalization Bounds
Department of Computer Science and Software Engineering Université Laval, Québec, Canada
Uncertainty Estimation and Generalization Bounds for Modern Deep Learning
Jun 11, 2026Luis A. OrtegaBayesian Neural Networks Neural Network Generalization
Department of Computer Science Machine Learning Group Madrid
PAC-Bayesian Adversarially Robust Generalization for Message Passing Graph Neural Networks: A Sensitivity Analysis
Jun 4, 2026Ziling Liang, Xinping Yi, Qingsong Wen +1PAC-Bayesian Generalization Bounds Adversarial Robustness
School of Information Science and Engineering, Southeast University, Nanjing 210096, China · Squirrel Ai Learning, Bellevue, WA, 98004 USA
Certified Policy Optimisation for Nested Causal Bandits via PAC-Bayes Risk
May 28, 2026Tim Woydt, Paul-David ZuercherPAC-Bayesian Generalization Bounds Multi-Armed Bandits
ProdAxon · Department of Computer Science Technical University Darmstadt · Institute for Manufacturing Department of Engineering University of Cambridge
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
From Privacy to Generalization: Linear Max-Information Bounds for Differentially Private Learning Algorithms
May 25, 2026Christoph H. Lampert, Max Cairney-Leeming, Hossein ZakeriniaPAC-Bayesian Generalization Bounds Differentially Private Stochastic Gradient Descent
Institute of Science and Technology Austria (ISTA) Klosterneuburg, Austria
MMD-Balls as Credal Sets: A PAC-Bayesian Framework for Epistemic Uncertainty in Test-Time Adaptation
May 20, 2026Ahanaf Hasan AriqDistribution Shift Robustness PAC-Bayesian Generalization Bounds
Ideal School and College
A Sharper Picture of Generalization in Transformers
May 20, 2026Paul Lintilhac, Sair ShaikhNeural Network Generalization PAC-Bayesian Generalization Bounds
Thayer School of Engineering Dartmouth College · Dartmouth College
Anytime PAC-Bayes for Constrained Density-Ratio Networks under Covariate Shift
May 17, 2026Paulo Akira F. Enabe, Rodrigo ProvasiPAC-Bayesian Generalization Bounds Domain Adaptation
Escola Politénica University of São Paulo Department of Structural and Geotechnical Engineering
A Survey on Data-Dependent Worst-Case Generalization Bounds
May 13, 2026Hubert Leroux, Jean Marcus, Julien RogerNeural Network Generalization PAC-Bayesian Generalization Bounds
´Ecole polytechnique Palaiseau, France
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
When Do Early-Exit Networks Generalize? A PAC-Bayesian Theory of Adaptive Depth
Apr 17, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuNeural Network Generalization PAC-Bayesian Generalization Bounds
The University of Hong Kong, Hong Kong, China · Brain Investing Limited, Hong Kong, China
How Learning Dynamics Drive Adversarially Robust Generalization?
Oct 10, 2024Yuelin Xu, Xiao ZhangAdversarial Training PAC-Bayesian Generalization Bounds
CISPA Helmholtz Center for Information Security, Saarbrücken, Germany

Sep 21, 2026, cs.LGJ/K move · Enter open · S save

PAC-Bayesian Meta-Learning for Few-Shot Identification of Linear Dynamical Systems

Chenfeng Huang, George Michailidis

Department of Statistics & Data Science University of California, Los Angeles

PAC-Bayesian Generalization Bounds Meta-Learning Few-Shot Learning System Identification

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Identifying linear time-invariant (LTI) dynamical systems is challenging when trajectories are short, noisy, or high-dimensional. Traditional system identification typically treats each system independently and cannot exploit shared structure across related systems. We propose PBML-LTI, a PAC-Bayesian meta-learning framework for few-shot LTI system identification that learns a transferable prior over task-specific dynamics while preserving task heterogeneity. Each task corresponds to an unknown LTI system, and the meta-learner uses training trajectories to learn a data-dependent prior over transition matrices. For a new system with limited data, PBML-LTI performs Bayesian adaptation under this prior to obtain a task-specific posterior, providing accurate estimates and principled uncertainty quantification. A key challenge is temporal dependence, since LTI trajectories violate the i.i.d. assumptions underlying most PAC-Bayes meta-learning analyses. We address this with a martingale PAC-Bayes analysis for dependent trajectory losses and derive a support-query predictive-risk bound that motivates a fit-KL meta-training objective. The bound clarifies the roles of empirical fit, posterior complexity, and prior quality in few-shot adaptation under sequential dependence. We further derive corollaries for transition-matrix recovery and multi-step trajectory prediction, connecting uncertainty-aware meta-identification with finite-sample guarantees for dependent dynamical data.

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