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Chenxin Ma

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3 papers

2 author rows

JMLR Journal 2018 Journal Article

CoCoA: A General Framework for Communication-Efficient Distributed Optimization

Virginia Smith
Simone Forte
Chenxin Ma
Martin Takáč
Michael I. Jordan
Martin Jaggi

The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning. We present a general-purpose framework for distributed computing environments, CoCoA, that has an efficient communication scheme and is applicable to a wide variety of problems in machine learning and signal processing. We extend the framework to cover general non-strongly-convex regularizers, including L1-regularized problems like lasso, sparse logistic regression, and elastic net regularization, and show how earlier work can be derived as a special case. We provide convergence guarantees for the class of convex regularized loss minimization objectives, leveraging a novel approach in handling non-strongly-convex regularizers and non-smooth loss functions. The resulting framework has markedly improved performance over state-of-the- art methods, as we illustrate with an extensive set of experiments on real distributed datasets. [abs] [ pdf ][ bib ] &copy JMLR 2018. ( edit, beta )

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JMLR Journal 2016 Journal Article

Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption

Chenxin Ma
Rachael Tappenden
Martin Takáč

In this paper we generalize the framework of the Feasible Descent Method (FDM) to a Randomized (R-FDM) and a Randomized Coordinate-wise Feasible Descent Method (RC-FDM) framework. We show that many machine learning algorithms, including the famous SDCA algorithm for optimizing the SVM dual problem, or the stochastic coordinate descent method for the LASSO problem, fits into the framework of RC-FDM. We prove linear convergence for both R-FDM and RC-FDM under the weak strong convexity assumption. Moreover, we show that the duality gap converges linearly for RC-FDM, which implies that the duality gap also converges linearly for SDCA applied to the SVM dual problem. [abs] [ pdf ][ bib ] &copy JMLR 2016. ( edit, beta )

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ICML Conference 2015 Conference Paper

Adding vs. Averaging in Distributed Primal-Dual Optimization

Chenxin Ma
Virginia Smith
Martin Jaggi
Michael I. Jordan
Peter Richtárik
Martin Takác 0001

Distributed optimization methods for large-scale machine learning suffer from a communication bottleneck. It is difficult to reduce this bottleneck while still efficiently and accurately aggregating partial work from different machines. In this paper, we present a novel generalization of the recent communication-efficient primal-dual framework (COCOA) for distributed optimization. Our framework, COCOA+, allows for additive combination of local updates to the global parameters at each iteration, whereas previous schemes only allow conservative averaging. We give stronger (primal-dual) convergence rate guarantees for both COCOA as well as our new variants, and generalize the theory for both methods to cover non-smooth convex loss functions. We provide an extensive experimental comparison that shows the markedly improved performance of COCOA+ on several real-world distributed datasets, especially when scaling up the number of machines.

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