Gaussian Process Regression Networks
Abstract
We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.
Cite
@article{arxiv.1110.4411,
title = {Gaussian Process Regression Networks},
author = {Andrew Gordon Wilson and David A. Knowles and Zoubin Ghahramani},
journal= {arXiv preprint arXiv:1110.4411},
year = {2011}
}
Comments
17 pages, 3 figures, 1 table. Submitted for publication