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Empirische Inferenz Conference Paper 2002

Infinite Mixtures of Gaussian Process Experts

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We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.

Author(s): Rasmussen, CE. and Ghahramani, Z.
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Year: 2002
Day: 0
Editors: Dietterich, Thomas G.; Becker, Suzanna; Ghahramani, Zoubin
Bibtex Type: Conference Paper (inproceedings)
Digital: 0
Electronic Archiving: grant_archive
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

BibTex 

@inproceedings{2297,
  title = {Infinite Mixtures of Gaussian Process Experts},
  abstract = {We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Using a input-dependent adaptation of the Dirichlet Process, we implement a gating network for an infinite number of Experts. Inference in this model may be done efficiently using a Markov Chain relying on Gibbs sampling. The model allows the effective covariance function to vary with the inputs, and may handle large datasets -- thus potentially overcoming two of the biggest hurdles with GP models. Simulations show the viability of this approach.},
  editors = {Dietterich, Thomas G.; Becker, Suzanna; Ghahramani,  Zoubin},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  year = {2002},
  slug = {2297},
  author = {Rasmussen, CE. and Ghahramani, Z.}
}