1st Edition
Empirical Processes and Statistical Reinforcement Learning A Festschrift in Honor of Michael R. Kosorok
About the Editors List of Contributors Empirical process and semiparametric inference A semi-parametric model for target localization in distributed systems Minimax Optimality of the Moderated MMD and Empirical Moderated MMD Based Two Sample Tests Causal inference, reinforcement learning, and artificial intelligence Statistical Inference in Reinforcement Learning: A Selective Survey Fair Sufficient Representation Learning Efficiently Learning Synthetic Control Models for High-dimensional Disaggregated Data A Selective Review on Causal Reinforcement Learning with Unmeasured Confounders Efficient learning using U-statistics with a valid instrumental variable Precision medicine Learning Individualized Treatment Rules with Optimal Treatment Grouping for Maximizing Mean Survival Time Statistical and Machine Learning in Individualized Clinical Decision Rules: Applications in Early Detection Introduction to Outcome Weighted Learning for Optimal Treatment Regimes Precision Medicine Meets Sports Analytics: Promise, Pitfalls, and Lessons from the Field Optimal treatment strategies for prioritized outcomes
Biography
Shuangge (Steven) Ma is a Professor of Biostatistics at the Yale School of Public Health. He was a Ph.D. student of Prof. Kosorok at the University of Wisconsin and worked with him on semiparametric modeling, survival analysis, and empirical processes.
Eric B. Laber is the James B. Duke Distinguished Professor of Statistical Sciences and Biostatistics and Bioinformatics at Duke University. He is a fellow of the American Statistical Association and International Statistical Institute as well as the recipient of the Gottfried E. Noether Award, the Raymond J. Carroll Award, and the American Statistical Association Outstanding Application Award.
