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Distributed Block-diagonal Approximation Methods for Regularized Empirical Risk Minimization

Ching-pei Lee (ching-pei***at***cs.wisc.edu)
Kai-Wei Chang (kw***at***kwchang.net)

Abstract: Designing distributed algorithms for empirical risk minimization (ERM) has become an active research topic in recent years because of the practical need to deal with the huge volume of data. In this paper, we propose a general framework for training an ERM model via solving its dual problem in parallel over multiple machines. Our method provides a versatile approach for many large-scale machine learning problems, including linear binary/multi-class classification, regression, and structured prediction. Comparing with existing approaches, we show that our method has faster convergence under weaker conditions both theoretically and empirically.

Keywords: Distributed optimization, Regularized empirical risk minimization, Linear convergence

Category 1: Convex and Nonsmooth Optimization (Convex Optimization )

Category 2: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation:

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Entry Submitted: 06/04/2017
Entry Accepted: 06/04/2017
Entry Last Modified: 09/09/2017

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