题    目：Transfer learning for high-dimensional data with heavy-tailed noise: A sparse convoluted         rank regression method

主讲人：闫怡博

单    位：华东师范大学

时    间：2025年11月28日 15:00

腾讯ID：553-189-970

摘  要:Transfer learning can leverage information from the source domain to improve the estimation or prediction accuracy of the target task. For the high-dimensional linear regression model with sub-Gaussian noise, so-called Trans-Lasso algorithm has been proposed to boost the learning performance on the target domain. However, such algorithm may not lead to efficient estimates when the errors are heavy-tailed. In this paper, we investigate the penalized convoluted rank regression (CRR) under   the transfer learning framework, aiming to provide robust estimators   when dealing with heavy-tailed noise. The convolution smoothing technique improves the smoothness of the loss function without  introducing any bias. In the high-dimensional setting, we first propose a transfer learning algorithm on the penalized CRR models with known transferable sources, and establish l2/l1-estimation error  bounds for the corresponding estimators. Besides, we propose a transferable detection method to select informative sources and also verify its consistency. At last, we demonstrate the validity and effectiveness of our proposed methods using simulated data and a real-world dataset concerning the associations among gene expressions.

简    介：闫怡博，华东师范大学统计学院博士后，主持一项国家自然科学基金青年项目，一项中国博士后科学基金面上资助项目，入选国家资助博士后研究人员计划C档和上海市“超级博士后”激励计划。主要工作集中在稳健统计机器学习、高维统计、分位数回归等领域，在机器学习顶刊JMLR、统计学重要期刊JSPI、 International Statistical Review等发表多篇论文。