Warped Gaussian Processes

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.

• Type: Conference
• Year: 2003
• Where: NIPS
• Authors: Edward Snelson, Carl Edward Rasmussen, Zoubin Ghahramani
• Keywords: GP Outputs, NIPS 2003, NIPS 2007, Nonlinear Transformation, Regular Gp