Derives smoothness-based PAC-Bayes derandomization bounds for deterministic predictors using Rademacher complexity of the Jensen gap class, yielding Jacobian/Hessian flatness terms and a practical regularizer tested on CIFAR-10.
Regularizing deep neural networks with stochastic estimators of hessian trace.arXiv preprint arXiv:2208.05924, 2022
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Function-space definitions of sharpness and complexity jointly explain more generalization variance than parameter-space versions, yet leave unexplained cases that suggest the two-factor view is incomplete.
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Smoothness-Based Derandomization of PAC-Bayes Bounds
Derives smoothness-based PAC-Bayes derandomization bounds for deterministic predictors using Rademacher complexity of the Jensen gap class, yielding Jacobian/Hessian flatness terms and a practical regularizer tested on CIFAR-10.
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How Far Can Sharpness and Complexity Jointly Explain Generalization?
Function-space definitions of sharpness and complexity jointly explain more generalization variance than parameter-space versions, yet leave unexplained cases that suggest the two-factor view is incomplete.