Variational Gaussian Processes: A Functional Analysis View

George Wynne, Veit Wild
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:4955-4971, 2022.

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 this Paper

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. } }
Endnote
%0 Conference Paper %T Variational Gaussian Processes: A Functional Analysis View %A George Wynne %A Veit Wild %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-wynne22a %I PMLR %P 4955--4971 %U https://proceedings.mlr.press/v151/wynne22a.html %V 151 %X 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.
APA
Wynne, G. & Wild, V.. (2022). Variational Gaussian Processes: A Functional Analysis View . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:4955-4971 Available from https://proceedings.mlr.press/v151/wynne22a.html.

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