From: NeurIPS.cc/2021/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: This paper proposes an approximation method for the Wasserstein and Bures distances in high dimensions, and uses a permutation test to assess the frequency of labels for MNIST images. The approach is interesting, but reviewers have raised important issues in the primary development ranging from the computational difficulty of the approximation approach to lacking empirical details. Most importantly, the implications of the sensitivity results are unclear—particularly as they depend on various hyperparameter and kernel choices—and it is difficult to understand how these results connect to the approximation of the Wasserstein distance. Authors should incorporate this feedback and explicitly address the logical jumps and limitations of the proposed approach.