A Primal-Dual Algorithm for Faster Distributionally Robust Optimization
<|reference_start|>A Primal-Dual Algorithm for Faster Distributionally Robust Optimization: We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses the $f$-DRO, Wasserstein-DRO, and spectral/$L$-risk formulations used in practice. We present Drago, a stochastic primal-dual algorithm that achieves a state-of-the-art linear convergence rate on strongly convex-strongly concave DRO problems. The method combines both randomized and cyclic components with mini-batching, which effectively handles the unique asymmetric nature of the primal and dual problems in DRO. We support our theoretical results with numerical benchmarks in classification and regression.<|reference_end|>
@article{mehta2024a,
    title={A Primal-Dual Algorithm for Faster Distributionally Robust Optimization},
    author={Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui},
    journal={arXiv preprint arXiv:2403.10763},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.10763},
    primaryClass={stat.ML cs.LG math.OC}
}