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报告题目：Covariate-shift Robust Adaptive Transfer Learning for High-Dimensional Regression

报告人：刘婧媛

报告时间：2024年12月26日 15：00~16：00

报告地点：腾讯会议（#腾讯会议：211-616-902）

报告内容简介：The main challenge that sets transfer learning apart from traditional supervised learning is the distribution shift, reflected as the shift between the source and target models and that between the marginal covariate distributions. High-dimensional data introduces unique challenges, such as covariate shifts in the covariate correlation structure and model shifts across individual features in the model. In this work, we tackle model shifts in the presence of covariate shifts in the high-dimensional regression setting. Furthermore, to learn transferable information which may vary across features, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise transferable structures. Non-asymptotic bound is provided for the estimation error of the target model, showing the robustness of the proposed method to high-dimensional covariate shifts.

报告人简介：刘婧媛，厦门大学经济学院统计学与数据科学系教授、博士生导师，国家级青年人才（教育部），厦门大学南强卓越教学名师，厦门大学南强青年拔尖人才（A类），厦门大学“我最喜爱的十位教师”。美国宾夕法尼亚州立大学统计学博士。科研方面主要从事高维及复杂数据的统计方法、网络数据建模与推断、多数据源整合等领域的工作，在JASA，JOE, JBES等国际权威学术期刊发表论文30余篇，担任AOAS等权威期刊编委，入选福建省杰出青年科研人才计划。

邀请人：徐平峰（东北师范大学）