CUED Publications database

Stochastic Expectation Propagation for Large Scale Gaussian Process Classification

Hernández-Lobato, D and Hernández-Lobato, JM and Li, Y and Bui, T and Turner, RE Stochastic Expectation Propagation for Large Scale Gaussian Process Classification. (Unpublished)

Full text not available from this repository.

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.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 20:07
Last Modified: 27 Jul 2017 05:30
DOI: