Inter-domain Deep Gaussian Processes
Abstract
Inter-domain Gaussian processes (GPs) allow for high flexibility and low computational cost when performing approximate inference in GP models. They are particularly suitable for modeling data exhibiting global structure but are limited to stationary covariance functions and thus fail to model non-stationary data effectively. We propose Inter-domain Deep Gaussian Processes, an extension of inter-domain shallow GPs that combines the advantages of inter-domain and deep Gaussian processes (DGPs), and demonstrate how to leverage existing approximate inference methods to perform simple and scalable approximate inference using inter-domain features in DGPs. We assess the performance of our method on a range of regression tasks and demonstrate that it outperforms inter-domain shallow GPs and conventional DGPs on challenging large-scale real-world datasets exhibiting both global structure as well as a high-degree of non-stationarity.
Cite
@article{arxiv.2011.00415,
title = {Inter-domain Deep Gaussian Processes},
author = {Tim G. J. Rudner and Dino Sejdinovic and Yarin Gal},
journal= {arXiv preprint arXiv:2011.00415},
year = {2020}
}
Comments
Published in Proceedings of the 37th International Conference on Machine Learning (ICML 2020)