Online Seminar on Mathematical Foundations of Data Science(Math4DS)是在线的、每周举办的系列研讨会,其内容涵盖数据科学、机器学习、统计以及优化背后的数学基础。
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Math for DS 第三十八期线上直播预告
主题:Global Convergence of EM?
嘉宾:Harrison Zhou, Yale University
时间:北京时间4月9日23:00
地点:Zoom,公众号后台回复 Math4DS
主题介绍
In this talk I will first discuss a recent joint work with Yihong Wu: https://arxiv.org/abs/1908.10935. We show that the randomly initialized EM algorithm for parameter estimation in the symmetric two-component Gaussian mixtures converges to the MLE in at most $\sqrt(n)$ iterations with high probability. Then I will mention the limitations of that work and propose an extension to general Gaussian mixtures by overparameterization.嘉宾介绍
Harrison Zhou is a Henry Ford II Professor and Chair of the Department of Statistics and Data Science at Yale. His main research interests include asymptotic decision theory, large covariance matrices estimation, graphical models, Bayesian nonparametrics, statistical network analysis, sparse canonical correlation analysis and principal component analysis, and analysis of iterative algorithms. His research has been acknowledged with awards including the National Science foundation Career Award, the Noether Young Scholar Award from the American Statistical Association, the Tweedie Award, the IMS Medallion lecture and IMS Fellow from the Institute of mathematical Statistics.
直播地址
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https://space.bilibili.com/403058474
研讨会主办方简介
组织者:
Ethan X. Fang, Niao He, Junwei Lu, Zhaoran Wang, Zhuoran Yang, Tuo Zhao
赞助方:
参考文献
[1]https://sites.google.com/view/seminarmathdatascience/home