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.

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Text

Hensman et al. "Scalable Variational Gaussian Process Classification." International Conference on Artificial Intelligence and Statistics, 2015.

Markdown

[Hensman et al. "Scalable Variational Gaussian Process Classification." International Conference on Artificial Intelligence and Statistics, 2015.](https://mlanthology.org/aistats/2015/hensman2015aistats-scalable/)

BibTeX

@inproceedings{hensman2015aistats-scalable,
  title     = {{Scalable Variational Gaussian Process Classification}},
  author    = {Hensman, James and de G. Matthews, Alexander G. and Ghahramani, Zoubin},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year      = {2015},
  url       = {https://mlanthology.org/aistats/2015/hensman2015aistats-scalable/}
}