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.
Cite
Text
Wynne and Wild. " Variational Gaussian Processes: A Functional Analysis View ." Artificial Intelligence and Statistics, 2022.Markdown
[Wynne and Wild. " Variational Gaussian Processes: A Functional Analysis View ." Artificial Intelligence and Statistics, 2022.](https://mlanthology.org/aistats/2022/wynne2022aistats-variational/)BibTeX
@inproceedings{wynne2022aistats-variational,
title = {{ Variational Gaussian Processes: A Functional Analysis View }},
author = {Wynne, George and Wild, Veit},
booktitle = {Artificial Intelligence and Statistics},
year = {2022},
pages = {4955-4971},
volume = {151},
url = {https://mlanthology.org/aistats/2022/wynne2022aistats-variational/}
}