pac-bayes bound

Advances in PAC-Bayesian certification of deep neural networks: tighter closed-form inequalities and optimization of bounds on non-differentiable losses UAI 2026 General Bayesian Policy Learning UAI 2026 Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size? AAAI 2026 Learning to Sample in Stochastic Optimization UAI 2025 Pick-to-Learn and Self-Certified Gaussian Process Approximations AISTATS 2025 The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws AISTATS 2025 Better-than-KL PAC-Bayes Bounds COLT 2024 Compositional PAC-Bayes: Generalization of GNNs with persistence and beyond NeurIPS 2024 Controlling Multiple Errors Simultaneously with a PAC-Bayes Bound NeurIPS 2024 CR-SAM: Curvature Regularized Sharpness-Aware Minimization AAAI 2024 Improved Regret Bounds for Non-Convex Online-Within-Online Meta Learning ICLR 2024 Learning to Warm-Start Fixed-Point Optimization Algorithms JMLR 2024 Learning via Surrogate PAC-Bayes NeurIPS 2024 Length independent PAC-Bayes bounds for Simple RNNs AISTATS 2024 Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often! ICML 2024 More PAC-Bayes bounds: From bounded losses, to losses with general tail behaviors, to anytime validity JMLR 2024 PAC-Bayes-Chernoff bounds for unbounded losses NeurIPS 2024 PAC-Bayes Generalisation Bounds for Dynamical Systems including Stable RNNs AAAI 2024 Understanding prompt engineering may not require rethinking generalization ICLR 2024 A Unified Recipe for Deriving (Time-Uniform) PAC-Bayes Bounds JMLR 2023 Exponential Smoothing for Off-Policy Learning ICML 2023 Local Risk Bounds for Statistical Aggregation COLT 2023 Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel ICLR 2023 The Pick-to-Learn Algorithm: Empowering Compression for Tight Generalization Bounds and Improved Post-training Performance NeurIPS 2023 Tighter PAC-Bayes Bounds Through Coin-Betting COLT 2023