报告题目：Representation Transfer Learning for Semiparametric Regression
报告人：贺百花特任副教授
报告时间：2025年9月27日（周六）上午10:00—11:00
报告地点：藕舫楼724室
主持人：卫雨婷 讲师
报告简介：
We propose a transfer learning method that utilizes data representations in a semiparametric regression model. Our aim is to perform statistical inference on the parameter of primary interest in the target model while accounting for potential nonlinear effects of confounding variables. We leverage knowledge from source domains, assuming that the sample size of the source data is substantially larger than that of the target data. This knowledge transfer is carried out by the sharing of data representations, predicated on the idea that there exists a set of latent representations transferable from the source to the target domain. We address model heterogeneity between the source and target domains by incorporating domain-specific parameters in their respective models. We establish sufficient conditions for the identifiability of the models and demonstrate that the estimator for the primary parameter in the target model is both consistent and asymptotically normal. These results lay the theoretical groundwork for making statistical inferences about the main effects. Our simulation studies highlight the benefits of our method, and we further illustrate its practical applications using real-world data.
报告人简介：
贺百花，博士，中国科学技术大学特任副教授，主要从事模型平均以及多源域学习等领域的研究，相关论文发表在Journal of the American Statistical Association，Journal of Machine Learning Research，Biometrics，INFORMS Journal on Computing等统计和机器学习期刊，主持国家自然科学基金重大项目子课题以及青年基金，入选省部级人才计划支持。
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数学与统计学院
江苏省应用数学（南京信息工程大学）中心
江苏省系统建模与数据分析国际合作联合实验室
江苏省统计科学研究基地
2025年9月24日
