pac-bayes theory

Bound to Disagree: Generalization Bounds via Certifiable Surrogates UAI 2026 Sheaf Graph Neural Networks via PAC-Bayes Spectral Optimization AAAI 2026 The Geometry of Robustness: Optimizing Loss Landscape Curvature and Feature Manifold Alignment for Robust Finetuning of Vision-Language Models CVPR 2026 Deep Distributed Optimization for Large-Scale Quadratic Programming ICLR 2025 Statistical Guarantees for Lifelong Reinforcement Learning using PAC-Bayes Theory AISTATS 2025 Learning via Surrogate PAC-Bayes NeurIPS 2024 Length independent PAC-Bayes bounds for Simple RNNs AISTATS 2024 Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures AISTATS 2024 PAC-Bayes-Chernoff bounds for unbounded losses NeurIPS 2024 PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction NeurIPS 2023 PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023 Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice JMLR 2023 On Margins and Generalisation for Voting Classifiers NeurIPS 2022 PAC-Bayes Information Bottleneck ICLR 2022 Assessing Local Generalization Capability in Deep Models AISTATS 2020 Meta-Learning PAC-Bayes Priors in Model Averaging AAAI 2020 Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory ICML 2018 Exploring Generalization in Deep Learning NeurIPS 2017 Robust Forward Algorithms via PAC-Bayes and Laplace Distributions AISTATS 2014 PAC-Bayes-Empirical-Bernstein Inequality NeurIPS 2013 PAC-Bayes Bounds with Data Dependent Priors JMLR 2012 PAC-Bayes Bounds for the Risk of the Majority Vote and the Variance of the Gibbs Classifier NeurIPS 2006