Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Michalis Titsias RC AUEB, Miguel Lazaro-Gredilla


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.