Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression

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

Bibtex Metadata Paper Reviews Supplemental


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.