標題：Transfer Learning Under High-Dimensional Network Convolutional Regression Model
報告時間：2025年5月30日（星期五）10:00-11:00
報告地點：人民大街校區數學與統計學院415會議室
主講人： 黃丹陽
主辦單位：數學與統計學院
報告內容簡介：
Transfer learning enhances model performance by utilizing knowledge from related domains, particularly when labeled data is scarce. While existing research addresses transfer learning under various distribution shifts in independent settings, handling dependencies in networked data remains challenging. To address this challenge, we propose a high-dimensional transfer learning framework based on network convolutional regression (NCR), inspired by the success of graph convolutional networks (GCNs). The NCR model incorporates random network structure by allowing each node’s response to depend on its features and the aggregated features of its neighbors, capturing local dependencies effectively. Our methodology includes a two-step transfer learning algorithm that addresses domain shift between source and target networks, along with a source detection mechanism to identify informative domains. Theoretically, we analyze the lasso estimator in the context of a random graph based on the Erd?s–Rényi model assumption, demonstrating that transfer learning improves convergence rates when informative sources are present. Empirical evaluations, including simulations and a real-world application using Sina Weibo data, demonstrate substantial improvements in prediction accuracy, particularly when labeled data in the target domain is limited.
主講人簡介：
黃丹陽，中國人民大學統計學院教授，吳玉章青年學者，中國人民大學國家治理大數據和人工智能創新平臺北京市消費大數據監測子實驗室主任。主持國家自然科學基金面上項目、北京市社會科學基金重點項目等科研課題，入選北京市科協青年人才托舉工程，曾獲北京市優秀人才培養資助。從事復雜網絡模型、大規模數據計算等方向的理論研究，關注統計理論在中小企業數字化發展中的應用。研究成果30余篇發表于JRSSB、JASA、JOE、JBES等權威期刊。獨著專著《大規模網絡數據分析與空間自回歸模型》入選“京東統計學圖書熱賣榜”。獲北京高校青年教師教學基本功比賽二等獎、最受學生歡迎獎等多項省部級教學獎勵。
