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              07月05日15:00 Yuhong Yang:Optimal On-Line Treatment Allocations For Personalized Medicine (Recommendation, Advertisement or Policy)

              講座編號:jz-yjsb-2019-y048

              講座題目:Optimal On-Line Treatment Allocations For Personalize Medicin(Recommendation, Advertisement or Policy)

              主 讲 人:Yuhong Yang  School of Statistics University of Minnesota

              講座時間:20190705日(星期五)下午15:00

              講座地點:阜成路西校區綜合樓1116

              參加對象:數學與統計學院教師、研究生

              主辦單位:研究生院

              承辦單位:數學與統計學院

              主講人簡介:

              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.

              主講內容:

               In practice of medicine (as an example), multiple treatments are often available to treat individual patients. The task of identifying the best treatment for a specific patient is very challenging due to patient inhomogeneity. Multi-armed bandit with covariates provides 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 present new strategies to achieve asymptotically efficient or minimax optimal treatment allocations. Since many nonparametric and parametric methods in supervised learning may be applied to estimating the mean treatment outcome functions (in terms of the covariates) but guidance on how to choose among them is generally unavailable, we propose a model combining allocation strategy for adaptive performance and show its strong consistency. When the mean treatment outcome functions are smooth, rates of convergence can be studied to quantify the effectiveness of a treatment allocation rule in terms of the overall benefits the patients have received.  A multi-stage randomized allocation with arm elimination algorithm is proposed to combine the flexibility in treatment outcome function modeling and a theoretical guarantee of the overall treatment benefits. Numerical results are given to demonstrate the performance of the new strategies. The talk is based on joint work with Wei Qian.  

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