讲座题目：A Robust Transfer Learning Approach for High-dimensional Linear Regression to Support Integration of Multi-source Gene Expression Data
主讲人：秦国友 教授
主持人：唐炎林 教授
开始时间：2025-03-17 15:30
讲座地址：普陀校区理科大楼A1514
主办单位：统计学院
报告人简介：
秦国友，博士，复旦大学教授、博士生导师，公共卫生学院生物统计学教研室主任。主要从事生物统计学方法学和应用研究，包括真实世界研究和因果推断，临床试验、针对复杂数据、复杂统计模型的统计方法创新，以及生物统计学方法在医学和公共卫生领域的应用。纵向数据分析相关研究工作获得了2014年教育部高等学校科学研究优秀成果二等奖。在BMJ、Plos Medicine、JAMA Network Open、Briefings in Bioinformatics、Biometrics、Biostatistics和Statistics in Medicine等医学和生物统计权威期刊上发表论文100余篇。主要学术兼职：中华预防医学会生物统计学分会第一届青年委员会主任委员，中华预防医学会生物统计分会和中国卫生信息与健康医疗大数据学会统计理论与方法委员会常务委员、中国现场统计研究会多元分析应用专业委员会与全国工业统计学教学研究会健康医疗大数据学会常务理事，以及《中国卫生统计》、《中国预防医学》、《Biostatistics & Epidemiology》等杂志编委。
报告内容：
Transfer learning aims to integrate useful information from multi-source datasets to improve the learning performance of target data. This can be effectively applied in genomics when we learn the gene associations in a target tissue, and data from other tissues can be integrated. However, heavy-tail distribution and outliers are common in genomics data, which poses challenges to the effectiveness of current transfer learning approaches. In this paper, we study the transfer learning problem under high-dimensional linear models with t-distributed error (Trans-PtLR), which aims to improve the estimation and prediction of target data by borrowing information from useful source data and offering robustness to accommodate complex data with heavy tails and outliers. In the oracle case with known transferable source datasets, a transfer learning algorithm based on penalized maximum likelihood and expectation-maximization algorithm is established. To avoid including non-informative sources, we propose to select the transferable sources based on cross-validation. Extensive simulation experiments as well as an application demonstrate that Trans-PtLR demonstrates robustness and better performance of estimation and prediction when heavy-tail and outliers exist compared to transfer learning for linear regression model with normal error distribution.
