报告题目：Transfer learning on stratified data: joint estimation transferred from strata

摘 要：In this talk，we discuss the target model with the help of auxiliary models from different but possibly related groups. Inspired by transfer learning, we propose a method called joint estimation transferred from strata (JETS). To obtain a sparse solution, JETS constructs a penalized framework combining a term that penalizes the target model and an additional term that penalizes the differences between auxiliary models and the target model. In this way, JETS overcomes the challenge caused by the limited samples in high-dimensional study, and obtains stable and accurate estimates regardless of whether auxiliary samples contain noisy information. We demonstrate that this method enjoys the computational advantage of the traditional methods such as the lasso. During simulations and applications, the proposed method is compared with several existing methods and JETS outperforms others.

报告时间：2024年10月12日下午4:30--5:30

报告地点：统计与数据科学学院109会议室

主办单位：统计与数据科学学院

专家简介：杨玥含，中央财经大学教授，龙马青年学者，主要从事多重结构数据建模、因果推断、迁移学习等研究，作为独立作者、第一作者或通讯作者在统计学四大期刊 Journal of the American Statistical Association、Biometrika、经济学顶级期刊 Journal of Business and Economics Statistics，人工智能顶级期刊 Pattern Recognition、Knowledge-Based Systems 等期刊发表论文近40篇。