Recent Advances in Wasserstein Distributionally Robust Optimization

Monday, April 15, 2019 - 1:25pm - 2:25pm
Lind 305
Rui Gao (The University of Texas at Austin)
In this talk, we consider decision-making problems under data uncertainty. In particular, we study a framework, called Wasserstein distributionally robust optimization, that aims to find a decision that hedges against a set of distributions that are close to some nominal distribution in Wasserstein metric. This framework, although being an infinite dimensional optimization, has a finite-dimensional tractable reduction in various data-driven settings by virtue of duality, and is closely related to many regularization problems in statistical learning. I will discuss the generalization error bound and asymptotic properties of such framework, and compare it with other distributional uncertainty sets including divergence-based and moment-based sets. If time permits, I will talk about an application of the framework to robust classification with limited information.

Rui Gao is an Assistant Professor of Information, Risk, and Operations Management at the McCombs School of Business at the University of Texas at Austin. He received his Ph.D. in Operations Research from Georgia Institute of Technology in 2018. His current research interests lie in the intersection of decision-making under uncertainty and statistical learning, as well as their applications in data analytics. His work has been recognized with several INFORMS prize, including finalist in Nicholson student paper competition in 2016, runner-up in the Computing Society student paper prize in 2017, and winner of the Data Mining best paper award in 2017.