Infinite Mixtures of Gaussian Process Experts

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

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Carl Rasmussen, Zoubin Ghahramani


We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression models. Us- ing an input-dependent adaptation of the Dirichlet Process, we imple- ment 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 over- coming two of the biggest hurdles with GP models. Simulations show the viability of this approach.