Scalable variational Gaussian process classification

Data Science Institute

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

Scalable variational Gaussian process classification. / Hensman, James; Matthews, Alexander G.; Ghahramani, Zoubin.
In: Proceedings of Machine Learning Research, Vol. 38, 02.2015, p. 351-360.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Hensman, J, Matthews, AG & Ghahramani, Z 2015, 'Scalable variational Gaussian process classification', Proceedings of Machine Learning Research, vol. 38, pp. 351-360. <http://jmlr.org/proceedings/papers/v38/hensman15.pdf>

APA

Hensman, J., Matthews, A. G., & Ghahramani, Z. (2015). Scalable variational Gaussian process classification. Proceedings of Machine Learning Research, 38, 351-360. http://jmlr.org/proceedings/papers/v38/hensman15.pdf

Vancouver

Hensman J, Matthews AG, Ghahramani Z. Scalable variational Gaussian process classification. Proceedings of Machine Learning Research. 2015 Feb;38:351-360.

Author

Hensman, James ; Matthews, Alexander G. ; Ghahramani, Zoubin. / Scalable variational Gaussian process classification. In: Proceedings of Machine Learning Research. 2015 ; Vol. 38. pp. 351-360.

Bibtex

@article{70ca5b5f5fd64ff5bd72c76b14b9556f,

title = "Scalable variational Gaussian process classification",

abstract = "Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.",

author = "James Hensman and Matthews, {Alexander G.} and Zoubin Ghahramani",

year = "2015",

month = feb,

language = "English",

volume = "38",

pages = "351--360",

journal = "Proceedings of Machine Learning Research",

issn = "1938-7228",

}

RIS

TY - JOUR

T1 - Scalable variational Gaussian process classification

AU - Hensman, James

AU - Matthews, Alexander G.

AU - Ghahramani, Zoubin

PY - 2015/2

Y1 - 2015/2

N2 - Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.

AB - Gaussian process classification is a popular method with a number of appealing properties. We show how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets. Importantly, the variational formulation can be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.

M3 - Journal article

AN - SCOPUS:84954308123

VL - 38

SP - 351

EP - 360

JO - Proceedings of Machine Learning Research

JF - Proceedings of Machine Learning Research

SN - 1938-7228

ER -

Research

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