Uncertainty in Artificial Intelligence
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Fast Gaussian Process Posteriors with Product Trees
David Moore, Stuart Russell
Abstract:
Gaussian processes (GP) are a powerful tool for nonparametric regression; unfortunately, calcu- lating the posterior variance in a standard GP model requires time O(n2 ) in the size of the training set. Previous work by Shen et al. (2006) used a k-d tree structure to approximate the pos- terior mean in certain GP models. We extend this approach to achieve efficient approximation of the posterior covariance using a tree clustering on pairs of training points, and demonstrate sig- nificant improvements in performance with neg- ligible loss of accuracy.
Keywords:
Pages: 613-622
PS Link:
PDF Link: /papers/14/p613-moore.pdf
BibTex:
@INPROCEEDINGS{Moore14,
AUTHOR = "David Moore and Stuart Russell",
TITLE = "Fast Gaussian Process Posteriors with Product Trees",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "613--622"
}


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