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Warped Gaussian Processes

2004

Conference Paper

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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.

Author(s): Snelson, E. and Rasmussen, CE. and Ghahramani, Z.
Book Title: Advances in Neural Information Processing Systems 16
Journal: Advances in Neural Information Processing Systems 16
Pages: 337-344
Year: 2004
Month: June
Day: 0
Editors: Thrun, S., L.K. Saul, B. Sch{\"o}lkopf
Publisher: MIT Press

Department(s): Empirische Inferenz
Bibtex Type: Conference Paper (inproceedings)

Event Name: Seventeenth Annual Conference on Neural Information Processing Systems (NIPS 2003)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-20152-6
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{2298,
  title = {Warped Gaussian Processes},
  author = {Snelson, E. and Rasmussen, CE. and Ghahramani, Z.},
  journal = {Advances in Neural Information Processing Systems 16},
  booktitle = {Advances in Neural Information Processing Systems 16},
  pages = {337-344},
  editors = {Thrun, S., L.K. Saul, B. Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  month_numeric = {6}
}