From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Spotlight)
comment: This paper shows how on high-dimensional data, GD on polynomially tailed losses with importance-weighting leads to a classifier that provably outperforms the standard max-margin classifier (trained on the weighted logistic loss). Their theoretical observations are complemented by experiments that support this gap also for neural network training on CIFAR10 and CelebA. Interestingly, for poly-losses regularization via early stopping also seems to help, however, no explanation is given for this behavior.

The reviewers agree that this paper proposes a solid contribution and is a valuable addition to this workshop. We encourage the authors to address the feedback from the reviewers.