Konstantinos Pitas

Dissecting Non-Vacuous Generalization Bounds based on the Mean-Field Approximation

Explaining how overparametrized neural networks simultaneously achieve low risk and zero empirical risk on benchmark datasets is an open problem. PAC-Bayes bounds optimized using variational inference (VI) have been recently proposed as a promising direction in obtaining non-vacuous bounds. We show…