PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Additive Gaussian Processes
David Duvenaud, Hannes Nickisch and Carl Edward Rasmussen
Neural Information Processing Systems Volume 24, 2011.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:8445
Deposited By:David Duvenaud
Deposited On:08 January 2012