Reviews: Robust Hypothesis Testing Using Wasserstein Uncertainty Sets

The rebuttal addressed my technical concerns, and also I seemed to have misjudged the size of the contributions at first. My score has been updated. This paper studies the two-sample non-parametric hypothesis testing problem. Given two collections of probability distribution, the paper studies approximating the best detector against the worst distributions from both collections. A standard surrogate loss approximation is used to upper bound the worst case risk (the maximum of the type I and type II errors) with a convex surrogate function, which is known to yield a good solution.