Stochastic Constrained DRO with a Complexity Independent of Sample Size

Qi Qi, Jiameng Lyu, Kung-sik Chan, Er-Wei Bai, Tianbao Yang

Abstract: Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shifts between training and test sets, has received tremendous attention in recent years. In this paper, we propose and analyze stochastic algorithms that apply to both non-convex and convex losses for solving Kullback–Leibler divergence constrained DRO problem. Compared with existing methods solving this problem, such as primal-dual methods and large mini-batch methods, our stochastic algorithms not only enjoy competitive if not better complexity independent of sample size but also just require a constant batch size at every iteration, which is more practical for broad applications. We establish a nearly optimal complexity bound for finding an $\epsilon$-stationary solution for non-convex losses and an optimal complexity for finding an $\epsilon$-optimal solution for convex losses. Empirical studies demonstrate the effectiveness of the proposed algorithms for solving non-convex and convex constrained DRO problems.

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