时 间：2025年3月17日16:30-17:30
报告人：柏杨上海财经大学副教授
地 点：普陀校区理科大楼A1514
主持人：唐炎林华东师范大学教授
摘 要：
In this paper, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial benefits under certain conditions than another dataset with an identical distribution to the target dataset. This observation renders the commonly-used debiasing step in the arameter-based transfer learning algorithm unnecessary under some circumstances to the classification problem. In particular, we propose two adaptive transfer learning algorithms based on the functional Distance Weighted Discrimination (DWD) classifier for scenarios with and without prior knowledge regarding informative sources. Furthermore, we establish the upper bound on the excess risk of the proposed classifiers, providing the statistical gain via transfer learning mathematically provable. Simulation studies are conducted to thoroughly examine the finite-sample performance of the proposed algorithms. Finally, we implement the proposed method to Beijing air-quality data, and significantly improve the prediction of the PM2.5 level of a target station by effectively incorporating information from source datasets.
报告人简介：
柏杨，上海财经大学统计与管理学院副教授（长聘），博士生导师；中国现场统计研究会资源与环境统计分会副理事长。柏杨博士一直从事纵向（函数型）数据分析等统计建模方法与理论的研究工作。目前在包括JASA、Statistica Sinica、Scandinavian Journal of Statistics、NeuIPS等国际期刊及会议上发表论文30余篇，google 学术他引250余次。已经主持完成国家自然科学基金面上项目和青年项目各一项，第三完成人获得教育部2014年度高等学校科学研究优秀成果奖二等奖1项。