报告人：David Woodruff
报告地点：数学与统计学院501室
报告时间：2018年06月04日星期一10:00-11:00
邀请人：
报告摘要：
I will discuss how sketching or data dimensionality reduction techniques can be used to speed up well-studied algorithms for problems occurring in numerical linear algebra, such as least squares regression and approximate singular value decomposition. I will also discuss how they can be used to achieve very efficient algorithms for variants of these problems, such as robust regression. I will finally touch upon many recent applications of sketching to machine learning.
主讲人简介：
David Woodruff is an associate professor at Carnegie Mellon University Before that he was at IBM Almaden Research Center from August, 2007- August, 2017, which he joined after completing his Ph.D. at MIT in theoretical computer science. His research interests include communication complexity, data stream algorithms, machine learning, numerical linear algebra, sketching, and sparse recovery. He is the recipient of the 2014 Presburger Award and Best Paper Awards at STOC 2013 and PODS 2010. At IBM he was a member of the Academy of Technology and a Master Inventor.
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