Online Asynchronous Distributed Regression - Archive ouverte HAL Access content directly
Journal Articles Annales de l'ISUP Year : 2018

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.
Fichier principal
Vignette du fichier
Pages de DEP_8-V-64396_(2015-2019)-32 (1).pdf (11.5 Mo) Télécharger le fichier
Origin : Explicit agreement for this submission

Dates and versions

hal-01024673 , version 1 (16-07-2014)
hal-01024673 , version 2 (10-03-2022)

Identifiers

Cite

Gérard Biau, Ryad Zenine. Online Asynchronous Distributed Regression. Annales de l'ISUP, 2018, 62 (3), pp.29-58. ⟨hal-01024673v2⟩
440 View
151 Download

Altmetric

Share

Gmail Facebook X LinkedIn More