Generative Neural Networks for Sinkhorn Distributionally Robust Hypothesis Testing
This paper studies the Sinkhorn distributionally robust hypothesis testing (SDRHT) problem, seeking a robust detector against least-favorable distributions in Sinkhorn discrepancy-based ambiguity sets centered at the empirical distributions. Existing approaches solve this problem by solving large-scale conic programs, which are not scalable. To overcome this, we propose a generative framework that learns least-favorable distributions and supports efficient training and end-to-end sampling. For the Sinkhorn discrepancy-based ambiguity sets, we first derive an equivalent conditional-KL-divergence representation with respect to kernel-smoothed reference distributions. This property allows us...
| Authors | Fenglin Zhang, Teyan Liu, Jie Wang |
|---|---|
| Primary category | stat.ML |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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