Online Asynchronous Distributed Regression

Abstract : Distributed computing offers a high degree of flexibility to accommodate modern learning constraints and the ever increasing size of datasets involved in massive data issues. Drawing inspiration from the theory of distributed computation models developed in the context of gradient-type optimization algorithms, we present a consensus-based asynchronous distributed approach for nonparametric online regression and analyze some of its asymptotic properties. Substantial numerical evidence involving up to 28 parallel processors is provided on synthetic datasets to assess the excellent performance of our method, both in terms of computation time and prediction accuracy.
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Pré-publication, Document de travail
2014
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https://hal.archives-ouvertes.fr/hal-01024673
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Dernière modification le : vendredi 25 mai 2018 - 12:02:06
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  • HAL Id : hal-01024673, version 1
  • ARXIV : 1407.4373

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Gérard Biau, Ryad Zenine. Online Asynchronous Distributed Regression. 2014. 〈hal-01024673〉

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