报告地点：行建楼学术活动室526
邀请人：孙海琳教授
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.
This is a joint work with Jie Wang and Yao Xie.
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).
