Enhancing Reinforcement Learning Models: Addressing Safety and Unobserved Confounders

：朱若青（伊利诺伊大学香槟分校）
：2024-05-30 15:00
：海韵园数理大楼686会议室

报告人：朱若青（伊利诺伊大学香槟分校）

时  间：2024年5月30日15:00

地  点：海韵园数理大楼686会议室

内容摘要:

Reinforcement learning has become an essential and powerful tool for modeling sequential decision data and inferring the optimal decision rule. Although enjoying enormous success in various fields, it still faces critical challenges in applications in medicine and human behavior studies, where the sample size can be small, noise is large, and unobserved confounders could be present. In this talk, we introduce two recent works that separately address two issues. One is a regularized framework that leads to more conservative and potentially safer treatment rules. This method is applied to an insulin dose-finding problem for diabetic management. Another is proposed to address the unobserved confounding issue in a partially observed Markov decision process setting. We utilize the proximal causal framework to estimate the value function of any potential treatment strategy. This approach is applied to a family relationship study that aims to understand the strategies for improving romantic relationships. 

个人简介：

朱若青，伊利诺伊大学香槟分校副教授。2013年在北卡罗来纳大学教堂山分校获得生物统计学博士学位，2013-2015年在耶鲁大学担任博士后研究助理，之后加入伊利诺伊大学香槟分校统计学系。主要研究兴趣为个性化医疗、强化学习、随机森林、生存分析、充分降维以及机器学习在传染病、营养健康、诊断和癌症等生物医学问题中的应用。曾在JASA、JRSSB、Sinica、Biometrics、Biostatistics等统计学一流期刊发表论文三十余篇。他目前还在伊利诺伊州卡莱医学院、国家超级计算应用中心和Carl R. Woese基因组生物学研究所任职，并且担任Journal of the American Statistical Association和Statistical Analysis and Data Mining的副主编。

 

联系人：梁薇