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Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server

Abstract:

This paper makes two contributions to Bayesian machine learning algorithms. Firstly, we propose stochastic natural gradient expectation propagation (SNEP), a novel alternative to expectation propagation (EP), a popular variational inference algorithm. SNEP is a black box variational algorithm, in that it does not require any simplifying assumptions on the distribution of interest, beyond the existence of some Monte Carlo sampler for estimating the moments of the EP tilted distributions. Furth...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Department:
MPLS; Statistics
Role:
Author
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Institution:
University of Oxford
Department:
MPLS; Engineering Sciences
Role:
Author
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Institution:
University of Oxford
Department:
MPLS; Statistics
Role:
Author
More by this author
Institution:
University of Oxford
Department:
MPLS; Statistics
Role:
Author
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Funding agency for:
Hasenclver, L
Grant:
EP/L016710/1
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Funding agency for:
Lienart, T
Grant:
EP/L505031/1
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Funding agency for:
Vollmer, S
Grant:
EP/K009850/1
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Publisher:
Journal of Machine Learning Research Publisher's website
Journal:
Journal of Machine Learning Research Journal website
Volume:
18
Issue:
106
Pages:
1-37
Publication date:
2017-10-24
Acceptance date:
2017-08-02
EISSN:
1533-7928
ISSN:
1532-4435
Source identifiers:
738504
Keywords:
Pubs id:
pubs:738504
UUID:
uuid:8963f732-32a9-4f37-9856-090390b0cec9
Local pid:
pubs:738504
Deposit date:
2017-10-25

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