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Benoît Patra

  1. A Discussion on Parallelization Schemes for Stochastic Vector Quantization Algorithms.

    Fri, 05/11/2012 - 15:01 — SomNambul
    Authors: Benoît Patra, Fabrice Rossi, Matthieu Durut
    Subjects: Machine Learning
    Abstract

    This paper studies parallelization schemes for stochastic Vector Quantization
    algorithms in order to obtain time speed-ups using distributed resources. We
    show that the most intuitive parallelization scheme does not lead to better
    performances than the sequential algorithm. Another distributed scheme is
    therefore introduced which obtains the expected speed-ups. Then, it is improved
    to fit implementation on distributed architectures where communications are
    slow and inter-machines synchronization too costly.

  2. Convergence of distributed asynchronous learning vector quantization algorithms.

    Fri, 12/24/2010 - 16:01 — SomNambul
    Authors: Benoît Patra
    Subjects: Statistics
    Abstract

    Motivated by the problem of effectively executing clustering algorithms on
    very large data sets, we address a model for large scale distributed clustering
    methods. To this end, we briefly recall some standards on the quantization
    problem and some results on the almost sure convergence of the Competitive
    Learning Vector Quantization (CLVQ) procedure. A general model for linear
    distributed asynchronous algorithms well adapted to several parallel computing
    architectures is also discussed.

  3. Sequential Quantile Prediction of Time Series.

    Wed, 08/19/2009 - 19:30 — SomNambul
    Authors: Gérard Biau, Benoît Patra
    Subjects: Methodology
    Abstract

    Motivated by a broad range of potential applications, we address the quantile
    prediction problem of real-valued time series. We present a sequential quantile
    forecasting model based on the combination of a set of elementary nearest
    neighbor-type predictors called "experts" and show its consistency under a
    minimum of conditions. Our approach builds on the methodology developed in
    recent years for prediction of individual sequences and exploits the quantile
    structure as a minimizer of the so-called pinball loss function.

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