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
Text
Wilson et al. "Gaussian Process Regression Networks." International Conference on Machine Learning, 2012.Markdown
[Wilson et al. "Gaussian Process Regression Networks." International Conference on Machine Learning, 2012.](https://mlanthology.org/icml/2012/wilson2012icml-gaussian/)BibTeX
@inproceedings{wilson2012icml-gaussian,
title = {{Gaussian Process Regression Networks}},
author = {Wilson, Andrew Gordon and Knowles, David A. and Ghahramani, Zoubin},
booktitle = {International Conference on Machine Learning},
year = {2012},
url = {https://mlanthology.org/icml/2012/wilson2012icml-gaussian/}
}