以优化交通和OT-Regularized Diversation为分布式强力优化的统计保障

Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences

We study finite-sample statistical performance guarantees for distributionally robust optimization (DRO) with optimal transport (OT) and OT-regularized divergence model neighborhoods. Specifically, we derive concentration inequalities for supervised learning via DRO-based adversarial training, as commonly employed to enhance the adversarial robustness of machine learning models. Our results apply to a wide range of OT cost functions, beyond the $p$-Wasserstein case studied by previous authors. I