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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

, , and . (2017)cite arxiv:1707.09564Comment: Accepted to ICLR 2018.

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A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks., , and . ICLR (Poster), OpenReview.net, (2018)Stochastic Nonconvex Optimization with Large Minibatches., and . ALT, volume 98 of Proceedings of Machine Learning Research, page 856-881. PMLR, (2019)Lower Bounds for Non-Convex Stochastic Optimization., , , , , and . CoRR, (2019)Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting., , , and . NeurIPS, page 20657-20668. (2021)A Stochastic Newton Algorithm for Distributed Convex Optimization., , , , and . NeurIPS, page 26818-26830. (2021)Does Invariant Risk Minimization Capture Invariance?, , , and . AISTATS, volume 130 of Proceedings of Machine Learning Research, page 4069-4077. PMLR, (2021)Learnability, Stability and Uniform Convergence, , , and . Journal of Machine Learning Research, (2010)The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication., , , , , , , and . CoRR, (2024)Noisy Interpolation Learning with Shallow Univariate ReLU Networks., , and . ICLR, OpenReview.net, (2024)Most Neural Networks Are Almost Learnable., , and . CoRR, (2023)