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.
Improving generalization of complex models under unbounded loss using PAC-Bayes bounds.Transactions on Machine Learning Research, 2024
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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.