Additive Gaussian Processes

2011

Conference Paper

ei


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.

Author(s): Duvenaud, D. and Nickisch, H. and Rasmussen, CA.
Book Title: Advances in Neural Information Processing Systems 24
Pages: 226-234
Year: 2011
Day: 0
Editors: J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger

Department(s): Empirical Inference
Research Project(s): Probabilistic Inference
Bibtex Type: Conference Paper (inproceedings)

Digital: 0
Event Name: Twenty-Fifth Annual Conference on Neural Information Processing Systems (NIPS 2011)
Event Place: Granada, Spain

Links: PDF
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BibTex

@inproceedings{DuvenaudNR2012,
  title = {Additive Gaussian Processes},
  author = {Duvenaud, D. and Nickisch, H. and Rasmussen, CA.},
  booktitle = {Advances in Neural Information Processing Systems 24},
  pages = {226-234},
  editors = {J Shawe-Taylor and RS Zemel and P Bartlett and F Pereira and KQ Weinberger},
  year = {2011}
}