统计与数据科学系系列学术报告之四百八十九期

时间： 2025年10月29日（周三）10:00-11:00

主持人：复旦大学管理学院统计与数据科学系郁文教授

地点：史带楼304室

报告人：Prof. Jianqing Fan

Princeton University

题目：Deep Transfer Offline Q-Learning under Nonstationary Environments

摘要：In dynamic decision-making scenarios across business and healthcare, leveraging sample trajectories from diverse populations can significantly enhance reinforcement learning (RL) performance for specific target populations. While existing transfer learning methods primarily focus on linear regression settings, they lack direct applicability to reinforcement learning algorithms. This paper pioneers the study of transfer learning for dynamic decision scenarios modeled by nonstationary finite-horizon Markov decision processes, utilizing neural networks as powerful function approximators and backward inductive learning. We demonstrate that naive sample pooling strategies, effective in regression settings, fail in Markov decision processes. To address this challenge, we introduce a novel “re-weighted targeting procedure” to construct “transferable RL samples” and propose “transfer deep Q-learning”, enabling neural network approximation with theoretical guarantees. We assume that the reward functions are transferable and deal with both situations in which the transition densities are transferable or nontransferable. Our analytical techniques for transfer learning in neural network approximation and transition density transfers have broader implications, extending to supervised transfer learning with neural networks and domain shift scenarios. Empirical experiments on both synthetic and real datasets corroborate the advantages of our method. (Joint work with Jinhang Chai and Elynn Chen)

个人简介：Jianqing Fan is the Frederick L. Moore Professor of Finance, Professor of Operations Research and Financial Engineering, and Former Chairman of the Department of Operations Research and Financial Engineering at Princeton University, where he directs both financial econometrics and statistics labs. After receiving his Ph.D. from the University of California at Berkeley, he was appointed as assistant, associate, and full professor at the University of North Carolina at Chapel Hill (1989-2003), professor at the University of California at Los Angeles (1997-2000), professor and chair at Chinese University of Hong Kong (2000-2003), and professor at the Princeton University (2003--). He was the past president of the Institute of Mathematical Statistics and the International Chinese Statistical Association. He is the joint editor of the Journal of the American Statistical Association and was the co-editor of The Annals of Statistics, Probability Theory and Related Fields, Econometrics Journal, Journal of Econometrics, and Journal of Business and Economics Statistics. His published work on statistics, machine learning, economics, finance, and computational biology has been recognized by The 2000 COPSS Presidents' Award, The 2007 Morningside Gold Medal of Applied Mathematics, Guggenheim Fellow in 2009, P.L. Hsu Prize in 2013, Royal Statistical Society Guy medal in silver in 2014, Noether Distinguished Scholar Award in 2018, Le Cam Award and Lecture in 2021, Frontiers of Science Award in 2024, and Wald Award and Lecture in 2025, and election to Academician of Academia Sinica and member of Royal Academy of Belgium, and follow of American Associations for Advancement of Science, Institute of Mathematical Statistics, American Statistical Association, and Society of Financial Econometrics. His research interests include high-dimensional statistics, data science, machine learning, mathematics of AI, financial economics, and computational biology. He coauthored 4 books and published over 300 highly cited papers.