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Conference Paper

Warped Gaussian Processes

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84156

Rasmussen,  CE
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Snelson, E., Rasmussen, C., & Ghahramani, Z. (2004). Warped Gaussian Processes. Advances in Neural Information Processing Systems 16, 337-344.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-D8FF-8
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.