Fantastic Generalization Measures are Nowhere to be Found

no code implementations • 24 Sep 2023 • Michael Gastpar, Ido Nachum, Jonathan Shafer, Thomas Weinberger

We study the notion of a generalization bound being uniformly tight, meaning that the difference between the bound and the population loss is small for all learning algorithms and all population distributions.

Generalization Bounds