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Activity Number: 258
Type: Contributed
Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract #314757
Title: A One-Shot Approach to Distributed Sparse Regression
Author(s): Jason Lee* and Yuekai Sun and Qiang Liu and Jonathan Taylor
Companies: and Stanford University and UC Irvine and Stanford University
Keywords: lasso ; communication-efficient ; distributed computing ; high-dimensional ; averaging
Abstract:

We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The main idea is to estimate the regression coefficients by averaging ``debiased'' lasso estimates. We show the approach recovers the convergence rate of the lasso when the number of machines grows slower than the square root of the size of the dataset.

Authors who are presenting talks have a * after their name.

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