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  Additive Gaussian Processes

Duvenaud, D., Nickisch, H., & Rasmussen, C. (2012). Additive Gaussian Processes. In J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, & K. Weinberger (Eds.), Advances in Neural Information Processing Systems 24 (pp. 226-234). Red Hook, NY, USA: Curran.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B874-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-19C2-8
Genre: Conference Paper

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 Creators:
Duvenaud, D, Author
Nickisch, H1, Author              
Rasmussen, CA, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.

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 Dates: 2012-01
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: DuvenaudNR2012
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Title: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Place of Event: Granada, Spain
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Title: Advances in Neural Information Processing Systems 24
Source Genre: Proceedings
 Creator(s):
Shawe-Taylor, J, Editor
Zemel, RS, Editor
Bartlett, P, Editor
Pereira, F, Editor
Weinberger, KQ, Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 226 - 234 Identifier: ISBN: 978-1-618-39599-3