AISTATS 2022poster6 citations
Variational Gaussian Processes: A Functional Analysis View
Abstract
Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference. This technique requires a user to select variational features to increase efficiency. So far the common choices in the literature are disparate and lacking generality. We propose to view the GP as lying in a Banach space which then facilitates a unified perspective. This is used to understand the relationship between existing features and to draw a connection between kernel ridge regression and variational GP approximations.
BibTeX
@InProceedings{pmlr-v151-wynne22a,
title = { Variational Gaussian Processes: A Functional Analysis View },
author = {Wynne, George and Wild, Veit},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {4955--4971},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/wynne22a/wynne22a.pdf},
url = {https://proceedings.mlr.press/v151/wynne22a.html},
abstract = { Variational Gaussian process (GP) approximations have become a standard tool in fast GP inference. This technique requires a user to select variational features to increase efficiency. So far the common choices in the literature are disparate and lacking generality. We propose to view the GP as lying in a Banach space which then facilitates a unified perspective. This is used to understand the relationship between existing features and to draw a connection between kernel ridge regression and variational GP approximations. }
}