全天北京pk10连中50期 pk10每天赢100怎么止损 北京PK10官方开奖网 8码滚雪球不连挂方法 北京pk10手机历史记录 pk101000本金怎么玩 北京pk10单双计算 pk10最牛稳赚5码计划 北京pk赛车一天开几期 北京pk10和值 北京pk赛车一天180期 赛车北京pk10精准计划 pk10怎么玩才能赚钱 北京pk10每天开多少期 北京pk10免费全天计划 去一尾什么意思图解 北京pk10公式大全图解 北京pk10赛车盛源 pk10冠亚和11算小稳赢 pk10一天赚300好搞吗 北京pk10网赌 北京PK10官网开奖 北京赛車pk10记录 pk10带人上岸是真的吗 pk10大小单双压彩技巧 顶尖职业赌客的感悟 北京pk10能不能作假 北京pk赛车10官网 北京pk免费计划APP 玩北京pk10哪个平台好
您所在的位置: 首页 / 讲座报告

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

讲座编号:jz-yjsb-2019-y048

讲座题目:Optimal On-Line Treatment Allocations For Personalize Medicin(Recommendation, Advertisement or Policy)

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

讲座时间:20190705日(星期五)下午15:00

讲座地点:阜成路西校区综合楼1116

参加对象:数学与统计学?#33322;?#24072;、研究生

主办单位:研究生院

承办单位:数学与统计学院

主讲人简介:

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.

主讲内容:

 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.  

北京pk10冠军单双技巧
法兰克福金融管理大学 编写时时彩计划公式 捷豹的传说在线客服 星际争霸重制版什么时候出 幸运数字最准确的方法是几 国米vs恩波利直播 河南快赢481开奖视 乐游棋牌官方下载 qq游戏襄樊卡五星麻将 金鱼卡通图片 法兰克福学派的代表人物有 星际争霸2壁纸