报告题目：Personalized Treatment Allocations Based on Delayed Multi-Armed Bandits with Covariates
报 告 人：Yuhong Yang 教授 明尼苏达大学
报告时间：2022年9月7日 下午16：00-17：00
报告地点：腾讯会议 ID：566-492-408
会议链接：https://meeting.tencent.com/dm/0rDGqgfYsPA6
校内联系人：韩月才 hanyc@jlu.edu.cn
报告摘要：In practice of medicine (and many other fields), multiple treatments (in a broad sense) are often available to treat individual patients (or subjects, customers etc). The task of online identification of the best treatment for a specific patient is very challenging due to patient inhomogeneity. Multi-armed bandits with covariates (MABC), also called contextual bandits, provide a framework for designing effective treatment allocation rules in a way that integrates the learning from experimentation with maximizing the benefits to the patients along the process. In this talk, we review basics of MABC and present some randomized (or epsilon-greedy) non-parametric strategies to achieve strongly consistent or minimax optimal treatment allocations, possibly with delayed observations of the outcomes. We illustrate how the nature of delay, smoothness of the mean reward functions and the randomization probability jointly affect the accumulated reward. Simulations and a real data example are given to demonstrate the performance of the proposed MABC methods. The talk is based on joint work with Dan Zhu, Wei Qian and Sakshi Arya.
报告人简介：Yuhong Yang received his Ph.D from Yale in statistics in 1996. He then joined Department of Statistics at Iowa State University and moved to the University of Minnesota in 2004. He has been full professor there since 2007. His research interests include model selection, multi-armed bandit problems, forecasting, high-dimensional data analysis, and machine learning. He has published in top journals in several fields, including Annals of Statistics, JASA, JRSSB, Biometrika, IEEE Transaction on Information Theory, Journal of Econometrics, Proceedings of AMS, Journal of Machine Leaning Research, and International Journal of Forecasting. He is a fellow of Institute of Mathematical Statistics and was a recipient of the US NSF CAREER Award.
