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Kernel-nased just-in-time learning for passing expectation propagation messages

Abstract:

We propose an efficient nonparametric strategy for learning a message operator in expectation propagation (EP), which takes as input the set of incoming messages to a factor node, and produces an outgoing message as output. This learned operator replaces the multivariate integral required in classical EP, which may not have an analytic expression. We use kernel-based regression, which is trained on a set of probability distributions representing the incoming messages, and the associated outgo...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher:
Association for Uncertainty in Artificial Intelligence
Host title:
31st Conference on Uncertainty in Artificial Intelligence, UAI, Amsterdam, Netherlands, July 12-16, 2015
Publication date:
2015-01-01
ISSN:
1525-3384
Pubs id:
pubs:577246
UUID:
uuid:61de56f1-24b4-43c7-81cc-6a1ec756b3bf
Local pid:
pubs:577246
Source identifiers:
577246
Deposit date:
2015-11-29

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