# Gaussian Process Random Fields

arXiv: https://arxiv.org/abs/1511.00054 · HTML: https://mlsanity.com/paper/1511.00054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2015.

## Built on by (most cited first, 2 of 2)

- [Gaussian Experts Selection using Graphical Models](https://mlsanity.com/paper/2102.01496/) (2021)
- [Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification](https://mlsanity.com/paper/2211.09940/) (2022)

## Abstract (arXiv)

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.