AI4OPT Seminar Series

Date: Thursday October 5, 2023

Time: 12:00 pm – 1:30 pm

Location: Love Manufacturing Building, Room 183 (771 Ferst Dr NW, Atlanta, GA 30332)

Speaker: Rui Gao

Optimal Robust Policy for Feature-Based Decision-Making

Abstract: We study policy optimization for feature-based decision-making, which seeks an end-to-end policy that renders an explicit mapping from features to decisions. Most existing works restrict the policies to some parametric class that may suffer from sub-optimality (such as affine class) or lack of interpretability (such as neural networks). Differently, we aim to optimize over all functions of features. In this case, the classical empirical risk minimization yields a policy that is not well-defined on unseen feature values. To avoid such degeneracy, we consider a Wasserstein distributionally robust framework. This leads to an adjustable robust optimization, whose optimal solutions are notoriously difficult to obtain except for a few notable cases. Perhaps surprisingly, we identify a new class of policies that are proven to be exactly optimal and can be computed efficiently. We compare our method with several benchmarks using synthetic and real data and demonstrate its superior empirical performance.

This is joint work with Luhao Zhang and Jincheng Yang.

Bio: 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. He graduated from ISyE in 2018, advised by Anton Kleywegt. His main research studies data-driven decision-making under uncertainty and prescriptive data analytics. His research has been recognized with several INFORMS paper competition awards, including 
Winner in Junior Faculty Interest Group Paper Competition (2020) and Finalist in George Nicholson Student Paper Competition (2016). He currently serves as an Associate Editor for Mathematical Programming.

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