報告題目：Covariate-shift robust adaptive transfer learning for high-dimensional regression
報 告 人：劉婧媛 教授 廈門大學
報告時間：2024年5月29日 14:00-15:00
報告地點：#騰訊會議：273-592-244
校内聯系人：朱複康 fzhu@jlu.edu.cn
報告摘要：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.
報告人簡介：劉婧媛，廈門大學經濟學院統計學與數據科學系教授、博士生導師，教育部青年長江學者，廈門大學南強卓越教學名師，廈門大學南強青年拔尖人才A類。美國賓夕法尼亞州立大學統計學博士。科研方面主要從事高維及複雜數據的統計方法、穩定性建模、統計基因學等領域的工作，在JASA，JOE, JBES等國際權威學術期刊發表論文30餘篇，擔任AOAS等期刊編委，入選福建省傑出青年科研人才計劃。
