Uncertainty in Artificial Intelligence
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Super-Samples from Kernel Herding
Yutian Chen, Max Welling, Alex Smola
Abstract:
We extend the herding algorithm to continuous spaces by using the kernel trick. The resulting "kernel herding" algorithm is an infinite memory deterministic process that learns to approximate a PDF with a collection of samples. We show that kernel herding decreases the error of expectations of functions in the Hilbert space at a rate O(1/T )which ismuch faster than the usual O(1/pT) for iid random samples. We illustrate kernel herding by approximating Bayesian predictive distributions.
Keywords:
Pages: 109-116
PS Link:
PDF Link: /papers/10/p109-chen.pdf
BibTex:
@INPROCEEDINGS{Chen10,
AUTHOR = "Yutian Chen and Max Welling and Alex Smola",
TITLE = "Super-Samples from Kernel Herding",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "109--116"
}


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