NeurIPS 2020

Sharp uniform convergence bounds through empirical centralization

Meta Review

The reviewers all agree that this work makes a valuable contribution to the literature on uniform concentration bounds, and all recommend acceptance. The paper studies an alternative approach to concentration via shifting functions by their empirical mean (whereas prior work had considered shifting by their true mean). This can lead to sharper data-dependent bounds.