CUED Publications database

Scalable gaussian process classification via expectation propagation

Hernández-Lobato, D and Hernández-Lobato, JM (2016) Scalable gaussian process classification via expectation propagation. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016. pp. 168-176.

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Copyright 2016 by the authors. Variational methods have been recently considered for scaling the training process of Gaussian process classifiers to large datasets. As an alternative, we describe here how to train these classifiers efficiently using expectation propagation (EP). The proposed EP method allows to train Gaussian process classifiers on very large datasets, with millions of instances, that were out of the reach of previous implementations of EP. More precisely, it can be used for (i) training in a distributed fashion where the data instances are sent to different nodes in which the required computations are carried out, and for (ii) maximizing an estimate of the marginal likelihood using a stochastic approximation of the gradient. Several experiments involving large datasets show that the method described is competitive with the variational approach.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 20:07
Last Modified: 18 Aug 2020 12:26