Information consistency of nonparametric Gaussian process methods

author: Matthias W. Seeger, Laboratory for Probabilistic Machine Learning, École Polytechnique Fédérale de Lausanne
published: Aug. 13, 2008,   recorded: July 2008,   views: 3196


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We present information consistency results for nonparametric sequential prediction with Gaussian processes. The connection to nonparametric MDL is through the prequential approach, as detailed in Gruenwald's 2007 book, Sect. 13.5. Our proof technique is elementary, making use of a convex duality previously useful to obtain PAC-Bayesian bounds. We also obtain precise information consistency rates for a wide range of kernels and input distributions, using kernel eigenvalue asymptotics. In all these cases, the linear expert space is an infinite-dimensional function space, but still very reasonable rates are obtained.

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