【专家简介】：毛晓军，上海交通大学长聘教轨副教授。他的研究领域包括分布式统计推断，推荐系统和高维数据分析。主要研究成果已经发表于JASA, JMLR, IEEE TSP, ICML, WWW,《管理世界》等顶级期刊及会议上。入选2023年度上海市青年科技启明星计划，2019年度上海市青年科技英才扬帆计划。目前是国际重要学术期刊Journal of Multivariate Analysis的Early Career Advisory Board成员。
【报告摘要】：This paper is devoted to studying the semi-supervised sparse statistical inference in a distributed setup. An efficient multi-round distributed debiased estimator, which integrates both labeled and unlabelled data, is developed. We will show that the additional unlabeled data helps to improve the statistical rate of each round of iteration. Our approach offers tailored debiasing methods for $M$-estimation and generalized linear model according to the specific form of the loss function. Our method also applies to a non-smooth loss like absolute deviation loss. Furthermore, our algorithm is computationally efficient since it requires only one estimation of a high-dimensional inverse covariance matrix. We demonstrate the effectiveness of our method by presenting simulation studies and real data applications that highlight the benefits of incorporating unlabeled data.
时 间：2023年6月12日 9:00
地 点：位育楼417