Data-Driven Minimax Optimization with Expectation Constraints
研究带期望约束的数据驱动极小极大优化问题，提出新型最优原始-对偶算法，并通过数据驱动鲁棒定价和公平约束下的AUC最大化验证其有效性。
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.