Variational Gaussian Processes: A Functional Analysis View
Machine Learning
2021-10-26 v1 Machine Learning
Statistics Theory
Statistics Theory
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
@article{arxiv.2110.12798,
title = {Variational Gaussian Processes: A Functional Analysis View},
author = {Veit Wild and George Wynne},
journal= {arXiv preprint arXiv:2110.12798},
year = {2021}
}