时 间：2025年9月26日（周五）15：45 – 16：30
地 点：普陀校区理科大楼A1514室
报告人：沈娟 复旦大学副教授
主持人：马慧娟 华东师范大学副教授
摘 要：
The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. We propose a novel Bayesian transfer learning method named “CONCERT” to allow robust partial information transfer for high-dimensional data analysis. A conditional spike-and-slab prior is introduced in the joint distribution of target and source parameters for information transfer. By incorporating covariate-specific priors, we can characterize partial similarities and integrate source information collaboratively to improve the performance on the target. In contrast to existing work, the CONCERT is a one-step procedure, which achieves variable selection and information transfer simultaneously. We establish variable selection consistency, as well as estimation and prediction error bounds for CONCERT. Our theory demonstrates the covariate- specific benefit of transfer learning. To ensure the scalability of the algorithm, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantages of CONCERT over existing cutting-edge transfer learning methods. The work is joint with Ruqian Zhang, Yijiao Zhang, Annie Qu, and Zhongyi Zhu.
报告人简介：
沈娟，复旦大学统计与数据科学系副教授，博士生导师。研究兴趣包含贝叶斯变量选择，混合模型，亚组分析等。在JASA等国际统计期刊发表论文多篇，主持多项国家自然科学基金。