MCMC for variationally sparse Gaussian processes

Hensman, James and De Matthews, Alexander G. and Filippone, Maurizio and Ghahramani, Zoubin (2015) MCMC for variationally sparse Gaussian processes. In: Advances in Neural Information Processing Systems. Neural information processing systems foundation, CAN, pp. 1648-1656.

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Abstract

Gaussian process (GP) models form a core part of probabilistic machine learning. Considerable research effort has been made into attacking three issues with GP models: how to compute efficiently when the number of data is large; how to approximate the posterior when the likelihood is not Gaussian and how to estimate covariance function parameter posteriors. This paper simultaneously addresses these, using a variational approximation to the posterior which is sparse in support of the function but otherwise free-form. The result is a Hybrid Monte-Carlo sampling scheme which allows for a non-Gaussian approximation over the function values and covariance parameters simultaneously, with efficient computations based on inducing-point sparse GPs. Code to replicate each experiment in this paper is available at github.com/sparseMCMC.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1711
Subjects:
ID Code:
79550
Deposited By:
Deposited On:
16 May 2016 10:52
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Apr 2020 08:01