关于举行陈楷（中国人民大学）学术报告会的通知

报告主题：Enhancing efficiency and robustness in high-dimensional linear regression with additional               unlabeled data

报 告 人：陈楷 

报告时间：2024年9月20日（星期五）下午16:30-17:30

报告地点：37号楼3A02

邀 请 人：何志坚 教授

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                                                                数学学院

2024年 9月18日

报告摘要：

In semi-supervised learning, the prevailing understanding suggests that observing additional unlabeled samples improves estimation accuracy for linear parameters only in the case of model misspecification. This paper challenges this notion, demonstrating its inaccuracy in high dimensions. Initially focusing on a dense scenario, we introduce robust semi-supervised estimators for the regression coefficient without relying on sparse structures in the population slope. Even when the true underlying model is linear, we show that leveraging information from large-scale unlabeled data improves both estimation accuracy and inference robustness. Moreover, we propose semi-supervised methods with further enhanced efficiency in scenarios with a sparse linear slope. Diverging from the standard semi-supervised literature, we also allow for covariate shift. The performance of the proposed methods is illustrated through extensive numerical studies, including simulations and a real-data application to the AIDS Clinical Trials Group Protocol 175 (ACTG175).

报告人介绍：

陈楷，中国人民大学统计与大数据研究院在读博士生，研究兴趣为因果推断，高维统计。