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

Jitkrittum, W; Gretton, A; Heess, N; Eslami, SMA; Lakshminarayanan, B; Sejdinovic, D; Szabó, Z; (2015) Kernel-based just-in-time learning for passing expectation propagation messages. In: Meila, Marina and Heskes, Tom, (eds.) Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 ). (pp. pp. 405-414). AUAI Press: Virginia, USA. Green open access

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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 outgoing messages. The kernel approach has two main advantages: first, it is fast, as it is implemented using a novel two-layer random feature representation of the input message distributions; second, it has principled uncertainty estimates, and can be cheaply updated online, meaning it can request and incorporate new training data when it encounters inputs on which it is uncertain. In experiments, our approach is able to solve learning problems where a single message operator is required for multiple, substantially different data sets (logistic regression for a variety of classification problems), where it is essential to accurately assess uncertainty and to efficiently and robustly update the message operator.

Type: Proceedings paper
Title: Kernel-based just-in-time learning for passing expectation propagation messages
Event: Thirty-First Conference on Uncertainty in Artificial Intelligence (UAI'15 )
Open access status: An open access version is available from UCL Discovery
Publisher version: http://auai.org/uai2015/proceedings/papers/235.pdf
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1470010
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