管理科学与工程系学术讲座

题目: Bayesian Sequential Learning for Contextual Selection

演讲人: Xiaowei Zhang, Visiting Assistant Professor, City University of

Hong Kong

时间: 10:00 a.m., 28th, Dec., 2018

地点: Room 309, Tongji Building A 

Abstract:

A typical decision making problem in practice is to select the best from a collection of alternatives. The value of each alternative, however, is unknown and can only be learned via expensive sampling. Motivated by the emerging popularity of personalized decision making in various areas including healthcare and e-commerce, we study the selection problem in the presence of contextual information, where the best alternative is not universal but depends on certain covariates. Given a limited sampling budget, we propose an adaptive sampling strategy to efficiently learn the decision rule that specifies the best alternative for a given value of the covariates. The sampling strategy is developed via a nonparametric Bayesian approach and is shown to be asymptotically optimal. We demonstrate the usefulness of our methodology via a case study in personalized medicine for selecting the best cancer treatment regimen.

Bio:

Xiaowei Zhang, Ph.D.

Xiaowei Zhang received his Ph.D. in Management Science and Engineering and M.S. in Financial Mathematics, both from Stanford University. He is currently a Visiting Assistant Professor in the Department of Management Sciences at the City University of Hong Kong. His research interests include simulation optimization, sequential learning, and business analytics. His work has been published at leading journals such as Management Science and Mathematics of Operations Research.