Adaptive Cholesky Gaussian Processes

Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz, Wouter Boomsma, Jes Frellsen, Søren Hauberg

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Abstract

We present a method to approximate Gaussian process regression models to large datasets by considering only a subset of the data. Our approach is novel in that the size of the subset is selected on the fly during exact inference with little computational overhead. From an empirical observation that the log-marginal likelihood often exhibits a linear trend once a sufficient subset of a dataset has been observed, we conclude that many large datasets contain redundant information that only slightly affects the posterior. Based on this, we provide probabilistic bounds on the full model evidence that can identify such subsets. Remarkably, these bounds are largely composed of terms that appear in intermediate steps of the standard Cholesky decomposition, allowing us to modify the algorithm to adaptively stop the decomposition once enough data have been observed.
OriginalsprogEngelsk
TidsskriftProceedings of Machine Learning Research
Vol/bind206
Sider (fra-til)408--452
Antal sider44
ISSN2640-3498
StatusUdgivet - 2023
Begivenhed26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023 - Valencia, Spanien
Varighed: 25 apr. 202327 apr. 2023

Konference

Konference26th International Conference on Artificial Intelligence and Statistics, AISTATS 2023
Land/OmrådeSpanien
ByValencia
Periode25/04/202327/04/2023

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