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A Framework for Evaluating Approximation Methods for Gaussian Process Regression

Chalupka, Krzysztof and Williams, Christopher K. I. and Murray, Iain (2013) A Framework for Evaluating Approximation Methods for Gaussian Process Regression. Journal of Machine Learning Research, 14 . pp. 333-350. ISSN 1532-4435. https://resolver.caltech.edu/CaltechAUTHORS:20130404-141235986

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Abstract

Gaussian process (GP) predictors are an important component of many Bayesian approaches to machine learning. However, even a straightforward implementation of Gaussian process regression (GPR) requires O(n^2) space and O(n^3) time for a data set of n examples. Several approximation methods have been proposed, but there is a lack of understanding of the relative merits of the different approximations, and in what situations they are most useful. We recommend assessing the quality of the predictions obtained as a function of the compute time taken, and comparing to standard baselines (e.g., Subset of Data and FITC). We empirically investigate four different approximation algorithms on four different prediction problems, and make our code available to encourage future comparisons.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://jmlr.csail.mit.edu/papers/v14/chalupka13a.htmlPublisherArticle
ORCID:
AuthorORCID
Chalupka, Krzysztof0000-0002-1225-2112
Additional Information:© 2013 Krzysztof Chalupka, Christopher K. I. Williams and Iain Murray. Submitted November 2011; Revised June 2012, November 2012; Published February 2013. We thank the anonymous referees whose comments helped improve the paper. We also thank Carl Rasmussen, Ed Snelson and Joaquin Quiñinero-Candela for many discussions on the comparison of GP approximation methods. This work is supported in part by the IST Programme of the European Community, under the PASCAL2 Network of Excellence, IST-2007-216886. This publication only reflects the authors’ views.
Funders:
Funding AgencyGrant Number
European CommunityIST-2007-216886
Subject Keywords:Gaussian process regression; subset of data; FITC; local GP
Record Number:CaltechAUTHORS:20130404-141235986
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20130404-141235986
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:37769
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:04 Apr 2013 22:13
Last Modified:03 Oct 2019 04:50

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