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.