主题：Optimal Robust Policy for Feature-Based Newsvendor
时间：2023年4月3日（周一）10:00 -11:30
地点：管理学院 A523
主讲嘉宾：高睿 助理教授 得克萨斯大学奥斯汀分校
主持人：崔政 百人计划研究员 浙江大学管理学院
讲座摘要：
We study policy optimization for the feature-based newsvendor, which seeks an end-to-end policy that renders an explicit mapping from features to ordering 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. The optimal robust policy is obtained by extending an optimal robust in-sample policy to unobserved feature values in a particular way and can be interpreted as a Lipschitz regularized critical fractile of the empirical conditional demand distribution. We compare our method with several benchmarks using synthetic and real data and demonstrate its superior empirical performance.This is a joint work with Luhao Zhang and Jincheng Yang.
嘉宾简介：
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).
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