Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets

arXiv:2403.14822v1 Announce Type: cross
Abstract: We present a new framework to address the non-convex robust hypothesis testing problem, wherein the goal is to seek the optimal detector that minimizes the maximum of worst-case type-I and type-II risk functions. The distributional uncertainty sets are constructed to center around the empirical distribution derived from samples based on Sinkhorn discrepancy. Given that the objective involves non-convex, non-smooth probabilistic functions that are often intractable to optimize, existing methods resort to approximations rather than exact …

abstract arxiv case center cs.lg distribution framework functions hypothesis math.oc risk robust stat.ml testing type uncertainty