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Gert R. G. Lanckriet

  1. Universality, Characteristic Kernels and RKHS Embedding of Measures.

    Чт, 03/04/2010 - 16:01 — SomNambul
    Authors: Kenji Fukumizu, Bharath K. Sriperumbudur, Gert R. G. Lanckriet
    Subjects: Machine Learning
    Abstract

    A Hilbert space embedding for probability measures has recently been
    proposed, wherein any probability measure is represented as a mean element in a
    reproducing kernel Hilbert space (RKHS). Such an embedding has found
    applications in homogeneity testing, independence testing, dimensionality
    reduction, etc., with the requirement that the reproducing kernel is
    characteristic, i.e., the embedding is injective.

  2. On integral probability metrics, \phi-divergences and binary classification.

    Чт, 08/20/2009 - 23:06 — SomNambul
    Authors: Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur Gretton, Bernhard Schölkopf, Gert R. G. Lanckriet
    Subjects: Information Theory
    Abstract

    A class of distance measures on probabilities -- the integral probability
    metrics (IPMs) -- is addressed: these include the Wasserstein distance, Dudley
    metric, and Maximum Mean Discrepancy. IPMs have thus far mostly been used in
    more abstract settings, for instance as theoretical tools in mass
    transportation problems, and in metrizing the weak topology on the set of all
    Borel probability measures defined on a metric space.

Math-arch

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