CUED Publications database

Understanding probabilistic sparse Gaussian Process approximations

Bauer, M and Van Der Wilk, M and Rasmussen, CE (2016) Understanding probabilistic sparse Gaussian Process approximations. Advances in Neural Information Processing Systems. pp. 1533-1541. ISSN 1049-5258

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Abstract

© 2016 NIPS Foundation - All Rights Reserved. Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets. The Fully Independent Training Conditional (FITC) and the Variational Free Energy (VFE) approximations are two recent popular methods. Despite superficial similarities, these approximations have surprisingly different theoretical properties and behave differently in practice. We thoroughly investigate the two methods for regression both analytically and through illustrative examples, and draw conclusions to guide practical application.

Item Type: Article
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:26
Last Modified: 08 Aug 2017 01:52
DOI: