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Simone Forte

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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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ICML Conference 2016 Conference Paper

Primal-Dual Rates and Certificates

  • Celestine Dünner
  • Simone Forte
  • Martin Takác 0001
  • Martin Jaggi

We propose an algorithm-independent framework to equip existing optimization methods with primal-dual certificates. Such certificates and corresponding rate of convergence guarantees are important for practitioners to diagnose progress, in particular in machine learning applications. We obtain new primal-dual convergence rates, e. g. , for the Lasso as well as many L1, Elastic Net, group Lasso and TV-regularized problems. The theory applies to any norm-regularized generalized linear model. Our approach provides efficiently computable duality gaps which are globally defined, without modifying the original problems in the region of interest.

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