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Scalable variational Gaussian process classification

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>02/2015
<mark>Journal</mark>Proceedings of Machine Learning Research
Number of pages10
Pages (from-to)351-360
<mark>Original language</mark>English


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.