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Automated Augmented Conjugate Inference for Gaussian Processes

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Gaussian Processes are a tool of choice for modelling function with uncertainties. However, inference is only tractable analytically for the classical case of regression with Gaussian noise since all other likelihoods are not conjugate with the Gaussian prior.

In this talk I will show how one can transform a large class of likelihoods into conditional conjugate distributions by augmenting them with latent variables. These augmented models have the advantage that, while the posterior inference is still not fully analytic, the full conditionals are! Consequently, one can work easily (and efficiently!) with algorithms like Gibbs sampling or Coordinate Ascent VI (CAVI) and outperform existing inference methods.

Reference:

Galy-Fajou, Théo, Florian Wenzel, and Manfred Opper. “Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models.” International Conference on Artificial Intelligence and Statistics. PMLR , 2020. https://arxiv.org/abs/2002.11451

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

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