AISTATS 2015poster823 citations
Scalable Variational Gaussian Process Classification
James Hensman, Alexander Matthews, Zoubin Ghahramani
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, out-performing the state of the art on benchmark datasets. Importantly, the variational formulation an be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.
BibTeX
@InProceedings{pmlr-v38-hensman15,
title = {{Scalable Variational Gaussian Process Classification}},
author = {Hensman, James and Matthews, Alexander and Ghahramani, Zoubin},
booktitle = {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
pages = {351--360},
year = {2015},
editor = {Lebanon, Guy and Vishwanathan, S. V. N.},
volume = {38},
series = {Proceedings of Machine Learning Research},
address = {San Diego, California, USA},
month = {09--12 May},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v38/hensman15.pdf},
url = {https://proceedings.mlr.press/v38/hensman15.html},
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, out-performing the state of the art on benchmark datasets. Importantly, the variational formulation an be exploited to allow classification in problems with millions of data points, as we demonstrate in experiments.}
}