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Approximate Inference in Gaussian Process Models

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If you have a question about this talk, please contact David Duvenaud.

This week’s RCC will be an in-depth tutorial on approximate inference for Gaussian process models. We will be covering both sparse approximations, as well as approximate inference for non-Gaussian likelihoods. The tutorial will be given by Andrew McHutchon, and myself, David Duvenaud.

The papers we will cover are:

A unifying view of sparse approximate Gaussian process regression. J. QuiƱonero-Candela and C. E. Rasmussen. http://jmlr.csail.mit.edu/papers/volume6/quinonero-candela05a/quinonero-candela05a.pdf

and

Approximations for binary Gaussian process classification. H. Nickisch and C. E. Rasmussen. http://jmlr.csail.mit.edu/papers/volume9/nickisch08a/nickisch08a.pdf (Sections 1-3 would be the most fruitful to read ahead of time)

This talk is part of the Machine Learning Reading Group @ CUED series.

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