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Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
Michalis Titsias · Miguel Lazaro-Gredilla

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor

We introduce a novel variational method that allows to approximately integrate out kernel hyperparameters, such as length-scales, in Gaussian process regression. This approach consists of a novel variant of the variational framework that has been recently developed for the Gaussian process latent variable model which additionally makes use of a standardised representation of the Gaussian process. We consider this technique for learning Mahalanobis distance metrics in a Gaussian process regression setting and provide experimental evaluations and comparisons with existing methods by considering datasets with high-dimensional inputs.

Author Information

Michalis Titsias (DeepMind)
Miguel Lazaro-Gredilla (Google DeepMind)

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