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Gaussian Process Regression Networks

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  • Andrew Gordon Wilson
  • David A. Knowles
  • Zoubin Ghahramani

Abstract

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

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  • Andrew Gordon Wilson & David A. Knowles & Zoubin Ghahramani, 2011. "Gaussian Process Regression Networks," Papers 1110.4411, arXiv.org.
  • Handle: RePEc:arx:papers:1110.4411
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    References listed on IDEAS

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    Cited by:

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