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Communication-efficient sparse regression: a one-shot approach

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arxiv 1503.04337 v3 pith:QZ2KCGYT submitted 2015-03-14 stat.ML cs.LG

Jason D. Lee , Yuekai Sun , Qiang Liu , Jonathan E. Taylor This is my paper · ORCID
classification stat.MLcs.LG
keywords approachlassoone-shotregressionsparseacrossaveragecommunication-efficient
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We devise a one-shot approach to distributed sparse regression in the high-dimensional setting. The key idea is to average "debiased" or "desparsified" lasso estimators. We show the approach converges at the same rate as the lasso as long as the dataset is not split across too many machines. We also extend the approach to generalized linear models.

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  1. Least Squares Approximation for a Distributed System

    stat.ME 2019-08 conditional novelty 5.0 of 10

    A distributed least squares approximation combines local estimators weighted by inverse covariance to match global estimator efficiency with one communication round.

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