十大彩票预测 学术报告[2025] 133号

(高水平大学建设系列报告1235号)

报告题目：Robust Multi-task Learning for Clustering and PCA

报告人：Haolei Weng Associate Professor（美国密歇根州立大学）

报告时间：2025年11月30日下午16:00-17:00

报告地点：深圳大学粤海校区汇星楼一号教室

内容摘要：In this talk, we discuss a general EM-flavored multi-task learning approach to learn mixture models. Our approach not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. We will focus on Gaussian mixture model to demonstrate our method and theory, and then briefly mention the extension to general mixture models. In the last part of the talk, we also discuss a suboptimal robustness issue for our approach and present our initial efforts to address this issue in the context of PCA.

报告人简历：Haolei Weng is currently an Associate Professor at the Department of Statistics and Probability, Michigan State University. Prior to MSU, he completed his Ph.D. in statistics from Columbia University in 2017 and was a postdoctoral researcher at Princeton University in 2018. Before going to Columbia, he received a B.S. in statistics from University of Science and Technology of China. His research interests are broadly in the area of high-dimensional statistics and statistical machine learning.

欢迎师生参加！

邀请人：胡湘红

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