ICML 2019oral218 citations
Rates of Convergence for Sparse Variational Gaussian Process Regression
David Burt, Carl Edward Rasmussen, Mark Van Der Wilk
Abstract
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ the number of
BibTeX
@InProceedings{pmlr-v97-burt19a,
title = {Rates of Convergence for Sparse Variational {G}aussian Process Regression},
author = {Burt, David and Rasmussen, Carl Edward and Van Der Wilk, Mark},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {862--871},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/burt19a/burt19a.pdf},
url = {https://proceedings.mlr.press/v97/burt19a.html},
abstract = {Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ the number of