Coresets for Wasserstein Distributionally Robust Optimization Problems Ruomin Huang 1 Jiawei Huang Hu Ding

Wasserstein distributionally robust optimization (WDRO) is a popular model to enhance the robustness of machine learning with ambiguous data. However, the complexity of WDRO can be prohibitive in practice since solving its "minimax" formulation requires a great amount of computation. Recently, several fast WDRO training algorithms for some specific machine learning tasks (e.g., logistic regression) have been developed. However, the research on designing efficient algorithms for general large-scale WDROs is still quite limited, to the best of our knowledge. Coreset is an important tool for compressing large dataset, and thus it has been widely applied to reduce the computational complexities for many optimization problems.