Gaussian processes for big data

Hensman, James and Fusi, Nicolò and Lawrence, Neil D. (2013) Gaussian processes for big data. In: Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013. UNSPECIFIED, USA, pp. 282-290.

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Abstract

We introduce stochastic variational inference for Gaussian process models. This enables the application of Gaussian process (GP) models to data sets containing millions of data points. We show how GPs can be variationally decomposed to depend on a set of globally relevant inducing variables which factorize the model in the necessary manner to perform variational inference. Our approach is readily extended to models with non-Gaussian likelihoods and latent variable models based around Gaussian processes. We demonstrate the approach on a simple toy problem and two real world data sets.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
Departments: Faculty of Health and Medicine > Medicine
ID Code: 85088
Deposited By: ep_importer_pure
Deposited On: 07 Mar 2017 11:10
Refereed?: Yes
Published?: Published
Last Modified: 17 Feb 2020 05:20
URI: https://eprints.lancs.ac.uk/id/eprint/85088

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