pac-bayes bound

Tighter PAC-Bayes Generalisation Bounds by Leveraging Example Difficulty AISTATS 2023 Toward Better PAC-Bayes Bounds for Uniformly Stable Algorithms NeurIPS 2023 A Unified View on PAC-Bayes Bounds for Meta-Learning ICML 2022 DNA: Domain Generalization with Diversified Neural Averaging ICML 2022 Does the Data Induce Capacity Control in Deep Learning? ICML 2022 Integral Probability Metrics PAC-Bayes Bounds NeurIPS 2022 On Margins and Derandomisation in PAC-Bayes AISTATS 2022 PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization NeurIPS 2022 Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables NeurIPS 2022 Estimating Lipschitz constants of monotone deep equilibrium models ICLR 2021 Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability NeurIPS 2021 How Tight Can PAC-Bayes be in the Small Data Regime? NeurIPS 2021 Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound NeurIPS 2021 On the Role of Data in PAC-Bayes Bounds AISTATS 2021 Speedy Performance Estimation for Neural Architecture Search NeurIPS 2021 Tighter Risk Certificates for Neural Networks JMLR 2021 A Limitation of the PAC-Bayes Framework NeurIPS 2020 Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation ICML 2020 Learning under Model Misspecification: Applications to Variational and Ensemble methods NeurIPS 2020 PAC-Bayes Analysis Beyond the Usual Bounds NeurIPS 2020 PAC-Bayes Learning Bounds for Sample-Dependent Priors NeurIPS 2020 Stochastic Neural Network with Kronecker Flow AISTATS 2020 Understanding Generalization in Deep Learning via Tensor Methods AISTATS 2020 Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence NeurIPS 2019 Deep learning generalizes because the parameter-function map is biased towards simple functions ICLR 2019