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统计学系系列短课程

时间：

Lecture 1  2016年5月12日（周四） 上午8:30-11:30

Lecture 2  2016年5月12日（周四） 下午1:30 - 3:30

地点：史带楼603室

Lecture 3  2016年5月13日（周五）上午8:30-11:30

地点：李达三楼104室

主持人：肖志国 副教授

主题：Lectures on Compressed Sensing and High-Dimensional Linear Regression

 

主讲嘉宾：Professor Tony Cai（蔡天文）    The Wharton School, University of Pennsylvania

 

摘要：The lectures will focus on compressed sensing and high dimensional linear regression. These and other related problems have attracted much recent interest in several fields including statistics, machine learning and electrical engineering, with a wide range of applications. In the high dimensional setting where the dimension p can be much larger than the sample size n, classical methods and results based on fixed p and large n are no longer applicable. We will analyze in detail the constrained and penalized    minimization methods for compressed sensing/high-dimensional regression and give a unified and elementary analysis on sparse signal recovery in three settings: noiseless, bounded noise and Gaussian noise. Time permitting, we will also discuss Johnson-Lindenstrauss Lemma and the construction of compressed sensing matrices.

                             

统计学系

2016-4-27