Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

TL;DR

This paper introduces stochastic expectation propagation for large-scale Gaussian process classification, reducing memory requirements and enabling training on very large datasets, outperforming traditional EP methods.

Contribution

The paper proposes a stochastic EP approach that mitigates linear memory scaling in Gaussian process classifiers, making large-scale training feasible.

Findings

Stochastic EP reduces memory usage compared to standard EP.

The method enables training on datasets previously infeasible for EP.

Performance is competitive with stochastic variational inference methods.

Abstract

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.