Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets

• URL: http://arxiv.org/abs/2403.14822v1
• Date: Thu, 21 Mar 2024 20:29:43 GMT
• Title: Non-Convex Robust Hypothesis Testing using Sinkhorn Uncertainty Sets
• Authors: Jie Wang, Rui Gao, Yao Xie,
• Abstract summary: We present a new framework to address the non-robust hypothesis testing problem. The goal is to seek the optimal detector that minimizes the maximum numerical risk.
• Score: 18.46110328123008
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 solutions. To tackle the challenge, we introduce an exact mixed-integer exponential conic reformulation of the problem, which can be solved into a global optimum with a moderate amount of input data. Subsequently, we propose a convex approximation, demonstrating its superiority over current state-of-the-art methodologies in literature. Furthermore, we establish connections between robust hypothesis testing and regularized formulations of non-robust risk functions, offering insightful interpretations. Our numerical study highlights the satisfactory testing performance and computational efficiency of the proposed framework.

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