报告人：Donglin Zeng
报告地点：zoom会议ID:968 4541 8828
报告时间：2022年11月08日星期二08:30-09:30
报告摘要:
One essential problem in precision medicine is to determine optimal Individualized Treatment Rules (ITRs) that tailor treatment decisions to patient-specific characteristics so as to maximize rewarding outcome. In practice, many treatment options are usually available, and this poses a significant challenge for learning reliable treatment rules given limited data. In this work, we propose GRoup Outcome Weighted Learning (GROWL) to simultaneously learn both group structure among the treatments and the resulting optimal group-based ITRs. Our approach combines treatment clustering and value optimization through one single algorithm. We provide theoretical guarantee for GROWL by establishing the results for Fisher consistency, excess risk bound, and non-asymptotic convergence rate. Extensive simulation studies and real data analysis are performed to demonstrate the superior performance of this method.
会议密码：162319
主讲人简介：
Donglin Zeng is professor of Biostatistics from the University of North Carolina at Chapel Hill. He obtained Phd of Statistics from the University of Michigan in 2001. He is a fellow of Institute of Mathematical Statistics and a fellow of American Statistical Association. His research interest includes machine learning, precision medicine, semiparametric models, high-dimensional inference and causal inference.
