@inproceedings{2298,
  title = {Warped Gaussian Processes},
  journal = {Advances in Neural Information Processing Systems 16},
  booktitle = {Advances in Neural Information Processing Systems 16},
  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.},
  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},
  author = {Snelson, E. and Rasmussen, CE. and Ghahramani, Z.},
  month_numeric = {6}
}