Gaussian Process Random Fields

Abstract

Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials. The GPRF likelihood is a simple, tractable, and parallelizeable approximation to the full GP marginal likelihood, enabling latent variable modeling and hyperparameter selection on large datasets. We demonstrate its effectiveness on synthetic spatial data as well as a real-world application to seismic event location.

Advances in Neural Information Processing Systems (NIPS), 2015

References in corpus (2)

• Loopy Belief Propagation for Approximate Inference: An Empirical Study
• Modelling local and global phenomena with sparse Gaussian processes

Cited by in corpus (2)

• Variational Fourier features for Gaussian processes
• Gaussian Experts Selection using Graphical Models