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A framework for distributed large-scale sparse regression
时间  Datetime
2018-04-17 14:00 — 15:00 
地点  Venue
Middle Lecture Room
报告人  Speaker
Leng Chenlei
单位  Affiliation
Professor of Statistics,University of Warwick
邀请人  Host
刘卫东
报告摘要  Abstract

An attractive approach for down-scaling a Big Data problem is to partition the dataset into subsets before fitting them via a divide and conquer approach. For a dataset with a large number of variables, this is best done via partitioning features, which however suffers from not taking correlations into account if not done properly. We propose a framework named DECO by applying a simple decorrelation step before performing sparse regression on each subset. The framework works for elliptically distributed features, heavy-tailed errors and a general class of sparsity penalties. Its performance is illustrated via sythesized and real data analysis. This is joint work with Xiangyu Wang at Google and David Dunson at Duke.