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Gérard Biau

 

Tuesday 13th January 2015

Time: 11am

 

B10 Basement Floor Seminar Room

Alexandra House, 17 Queen Square, London, WC1N 3AR

 

Distributed statistical algorithms

 

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, I will 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 the method, both in terms of computation time and prediction accuracy.