  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.
