Review for NeurIPS paper: Minimax Classification with 0-1 Loss and Performance Guarantees

Summary and Contributions: This paper presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss and family of rules. The goal of MRC is to find a classification rule that minimize the worst-case expected 0-1 loss with respect to a class of possible distributions. It first represents data, probability distributions and classification rules by matrices. The estimated classifier is cast as a linear optimization problem in which the uncertainty set is cast as the linear constraints. Some performance guarantees are proved, and numerical comparisons are conducted.