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The realm of data-driven optimization has garnered substantial attention in recent years. However, there remains a paucity of research addressing challenges posed by intricate data-driven constraints. In their paper titled “Data-Driven Minimax Optimization with Expectation Constraints,” Shuoguang Yang, Xudong Li, and Guanghui Lan concentrate on the realm of data-driven minimax optimization while considering expectation constraints. To grapple with these complex models, the authors introduce a novel class of optimal primal-dual algorithms. Their work showcases the practical efficacy of these algorithms through the resolution of real-world problems, including data-driven robust pricing and the maximization of the area under the ROC curve (AUC) while adhering to fairness constraints.
Yang et al. (Fri,) studied this question.