Review for NeurIPS paper: No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems

Strengths: This paper focus the hidden stratification problem of training deep neural networks with only coarse-grained class labels that results in variable performance across different subclasses. Motivated by the observation of feature representation of deep neural networks often capture information about unlabeled subclasses, this paper proposed GEORGE, a two-step method for mitigating hidden stratification. In the first step, GEORGE estimates subclass labels in feature space via Gaussian mixture model clustering. Then in the second step, the estimated subclass labels are used in a distributional robust optimization objecitve to train a robust classifier. The strengths of this work are: 1.This paper is overall complete.