標(biāo)題：Transfer Learning Under High-Dimensional Network Convolutional Regression Model
報(bào)告時(shí)間：2025年5月30日（星期五）10:00-11:00
報(bào)告地點(diǎn)：人民大街校區(qū)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院415會(huì)議室
主講人： 黃丹陽(yáng)
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
報(bào)告內(nèi)容簡(jiǎn)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(jiǎn)介：
黃丹陽(yáng)，中國(guó)人民大學(xué)統(tǒng)計(jì)學(xué)院教授，吳玉章青年學(xué)者，中國(guó)人民大學(xué)國(guó)家治理大數(shù)據(jù)和人工智能創(chuàng)新平臺(tái)北京市消費(fèi)大數(shù)據(jù)監(jiān)測(cè)子實(shí)驗(yàn)室主任。主持國(guó)家自然科學(xué)基金面上項(xiàng)目、北京市社會(huì)科學(xué)基金重點(diǎn)項(xiàng)目等科研課題，入選北京市科協(xié)青年人才托舉工程，曾獲北京市優(yōu)秀人才培養(yǎng)資助。從事復(fù)雜網(wǎng)絡(luò)模型、大規(guī)模數(shù)據(jù)計(jì)算等方向的理論研究，關(guān)注統(tǒng)計(jì)理論在中小企業(yè)數(shù)字化發(fā)展中的應(yīng)用。研究成果30余篇發(fā)表于JRSSB、JASA、JOE、JBES等權(quán)威期刊。獨(dú)著專(zhuān)著《大規(guī)模網(wǎng)絡(luò)數(shù)據(jù)分析與空間自回歸模型》入選“京東統(tǒng)計(jì)學(xué)圖書(shū)熱賣(mài)榜”。獲北京高校青年教師教學(xué)基本功比賽二等獎(jiǎng)、最受學(xué)生歡迎獎(jiǎng)等多項(xiàng)省部級(jí)教學(xué)獎(jiǎng)勵(lì)。
