Standard
Gaussian processes for big data. / Hensman, James; Fusi, Nicolò; Lawrence, Neil D.
Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013. 2013. p. 282-290.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Hensman, J, Fusi, N & Lawrence, ND 2013,
Gaussian processes for big data. in
Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013. pp. 282-290, 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013, Bellevue, WA, United States,
11/07/13.
APA
Hensman, J., Fusi, N., & Lawrence, N. D. (2013).
Gaussian processes for big data. In
Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013 (pp. 282-290)
Vancouver
Hensman J, Fusi N, Lawrence ND.
Gaussian processes for big data. In Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013. 2013. p. 282-290
Author
Hensman, James ; Fusi, Nicolò ; Lawrence, Neil D. /
Gaussian processes for big data. Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013. 2013. pp. 282-290
Bibtex
@inproceedings{ca1644c08f324411a5ddfa35211400d9,
title = "Gaussian processes for big data",
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.",
author = "James Hensman and Nicol{\`o} Fusi and Lawrence, {Neil D.}",
year = "2013",
language = "English",
pages = "282--290",
booktitle = "Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013",
note = "29th Conference on Uncertainty in Artificial Intelligence, UAI 2013 ; Conference date: 11-07-2013 Through 15-07-2013",
}
RIS
TY - GEN
T1 - Gaussian processes for big data
AU - Hensman, James
AU - Fusi, Nicolò
AU - Lawrence, Neil D.
PY - 2013
Y1 - 2013
N2 - 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.
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.
M3 - Conference contribution/Paper
AN - SCOPUS:84888155846
SP - 282
EP - 290
BT - Uncertainty in Artificial Intelligence - Proceedings of the 29th Conference, UAI 2013
T2 - 29th Conference on Uncertainty in Artificial Intelligence, UAI 2013
Y2 - 11 July 2013 through 15 July 2013
ER -
