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Rethinking Sharpness-Aware Minimization as Variational Inference
Sharpness-aware minimization (SAM) aims to improve the generalisation of gradient-based learning by seeking out flat minima.
Estimating optimal PAC-Bayes bounds with Hamiltonian Monte Carlo
An important yet underexplored question in the PAC-Bayes literature is how much tightness we lose by restricting the posterior family to factorized Gaussian distributions when optimizing a PAC-Bayes bound.
Understanding LLMs Requires More Than Statistical Generalization
The last decade has seen blossoming research in deep learning theory attempting to answer, "Why does deep learning generalize?"
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