报告题目：Personalized Treatment Allocations Based on Multi-Armed Bandits with Covariates
报告人：Yuhong Yang, Professor, University of Minnesota
腾讯会议号：436 496 800
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. 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.
个人简介：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.