Gaussian Processes for Big Data

James Hensman; Nicolò Fusi; Neil D. Lawrence

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Gaussian Processes for Big Data

James Hensman, Nicolò Fusi, Neil D. Lawrence

Uncertainty in Artificial Intelligence, AUAI Press 29, 2013.

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.

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Cite this Paper

BibTeX


@InProceedings{Hensman-bigdata13,
  title = 	 {{G}aussian Processes for Big Data},
  author = 	 {Hensman, James and Fusi, Nicolò and Lawrence, Neil D.},
  booktitle = 	 {Uncertainty in Artificial Intelligence},
  year = 	 {2013},
  editor = 	 {Nicholson, Ann and Smyth, Padhraic},
  volume = 	 {29},
  publisher =    {AUAI Press},
  pdf = 	 {http://auai.org/uai2013/prints/papers/244.pdf},
  url = 	 {/publications/gaussian-processes-for-big-data.html},
  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.
}
}

Endnote

%0 Conference Paper
%T Gaussian Processes for Big Data
%A James Hensman
%A Nicolò Fusi
%A Neil D. Lawrence
%B Uncertainty in Artificial Intelligence
%D 2013
%E Ann Nicholson
%E Padhraic Smyth	
%F Hensman-bigdata13
%I AUAI Press
%U /publications/gaussian-processes-for-big-data.html
%V 29
%X 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.

RIS


TY  - CPAPER
TI  - Gaussian Processes for Big Data
AU  - James Hensman
AU  - Nicolò Fusi
AU  - Neil D. Lawrence
BT  - Uncertainty in Artificial Intelligence
DA  - 2013/07/11
ED  - Ann Nicholson
ED  - Padhraic Smyth	
ID  - Hensman-bigdata13
PB  - AUAI Press
VL  - 29
L1  - http://auai.org/uai2013/prints/papers/244.pdf
UR  - /publications/gaussian-processes-for-big-data.html
AB  - 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.

ER  -

APA


Hensman, J., Fusi, N. & Lawrence, N.D.. (2013). Gaussian Processes for Big Data. Uncertainty in Artificial Intelligence 29 Available from /publications/gaussian-processes-for-big-data.html.