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Variational inference for scalable Gaussian process approximations

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If you have a question about this talk, please contact Louise Segar.

Gaussian processes are heavily used as nonparametric priors on functions. There are two challenges that are often relevant in this area: dealing with non-Gaussian likelihoods and scaling inference.

In this talk we discuss recent progress in using variational inference to meet these challenges. In the first part of the talk we resolve some theoretical issues around variational inference in infinite dimensional models. In the second part we give a variety of practical examples of the use of these approximations and share insights. Finally we will discuss GPflow a software library that implements these ideas using TensorFlow.

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

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