  A method for large scale Gaussian process classification has been recently
proposed based on expectation propagation (EP). Such a method allows Gaussian
process classifiers to be trained on very large datasets that were out of the
reach of previous deployments of EP and has been shown to be competitive with
related techniques based on stochastic variational inference. Nevertheless, the
memory resources required scale linearly with the dataset size, unlike in
variational methods. This is a severe limitation when the number of instances
is very large. Here we show that this problem is avoided when stochastic EP is
used to train the model.
