报告题目：Representation Transfer Learning for Semiparametric Regression
报告时间：2024年9月 20 日 （星期五）14:30-16:00
报告地点：翡翠湖校区科教楼B座1005
报 告 人：贺百花
工作单位：中国科学技术大学
主办单位：太阳成集团tyc9728太阳成集团tyc9728
内容简介：
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，中国科学技术大学特任副教授，本硕博毕业于武汉大学数学与统计学院，香港浸会大学博士后，主要研究方向为模型平均（Model Averaging）、高维统计分析（High-dimensional Statistics）、生存分析（Survival Analysis）。现主持国家自然科学基金青年项目，其研究成果发表于《Journal of the American Statistical Association》《Journal of Machine Learning Research》《INFORMS Journal on Computing》等国际高影响力期刊。
