Stochastic Expectation Propagation for Large Scale Gaussian Process Classification
Machine Learning
2015-12-07 v3
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.
Cite
@article{arxiv.1511.03249,
title = {Stochastic Expectation Propagation for Large Scale Gaussian Process Classification},
author = {Daniel Hernández-Lobato and José Miguel Hernández-Lobato and Yingzhen Li and Thang Bui and Richard E. Turner},
journal= {arXiv preprint arXiv:1511.03249},
year = {2015}
}