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