PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Warped Gaussian processes
Ed Snelson, Carl Edward Rasmussen and Zoubin Ghahramani
In: NIPS 2003, 9-11 Dec 2003, Vancouver, Canada.

Abstract

We generalise the Gaussian process (GP) framework for regression by learning a nonlinear transformation of the GP outputs. This allows for non-Gaussian processes and non-Gaussian noise. The learning algorithm chooses a nonlinear transformation such that transformed data is well-modelled by a GP. This can be seen as including a preprocessing transformation as an integral part of the probabilistic modelling problem, rather than as an ad-hoc step. We demonstrate on several real regression problems that learning the transformation can lead to significantly better performance than using a regular GP, or a GP with a fixed transformation.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:586
Deposited By:Ed Snelson
Deposited On:26 December 2004