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Joint Gaussian Process-Density Mixtures

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Gaussian Processes (GPs) provide a natural framework for Bayesian kernel methods. This talk will be about some work in progress on combining GPs with density estimation in a mixture model. The motivations are: using kernels tuned individually to each mixture component gives a more flexible input-output model, unlabelled data can be used in a semi-supervised setting and the computational complexity can be reduced because only examples belonging to the same mixture component need to be included in the kernel matrix for that mixture component. I will illustrate the idea with a regression example using a coarse two-stage approximation: density estimation followed by weighted GP predictions. A more principled variational Bayes treatment of the joint estimation problem shows how a low complexity solution can be obtained.

This talk is part of the Inference Group series.

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