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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.
@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}, slug = {2298}, author = {Snelson, E. and Rasmussen, CE. and Ghahramani, Z.}, month_numeric = {6} }