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报告题目：Sinkhorn Distributionally Robust Optimization

报告人：Rui Gao, Assistant Professor, University of Texas at Austin

主持人：王祥丰 副教授

报告时间：2023年3月27日（星期一）10:00-11:30

报告地点：普陀校区理科楼B112室

报告摘要：

We study distributionally robust optimization (DRO) with Sinkhorn distance -- a variant of Wasserstein distance based on entropic regularization. We provide convex programming dual reformulation for a general nominal distribution. Compared with Wasserstein DRO, it is computationally tractable for a larger class of loss functions, and its worst-case distribution is more reasonable. We propose an efficient first-order algorithm with bisection search to solve the dual reformulation. Finally, we provide various numerical examples using both synthetic and real data to demonstrate its competitive performance and light computational speed.

报告人简介：

Rui Gao is an Assistant Professor in the Department of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin. His main research studies data-driven decision-making under uncertainty and prescriptive data analytics. He received a Ph.D. in Operations Research from Georgia Institute of Technology in 2018, and a B.Sc. in Mathematics and Applied Mathematics from Xi'an Jiaotong University in 2013. He currently serves as an Associate Editor for Mathematical Programming. His research has been recognized with several INFORMS paper competition awards, including Winner in Junior Faculty Interest Group Paper Competition (2020), Winner in Data Mining Best Paper Award (2017), and Finalist in George Nicholson Student Paper Competition (2016).