Journal article
Distributed Bayesian learning with stochastic natural gradient expectation propagation and the posterior server
- Abstract:
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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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(Version of record, pdf, 961.9KB)
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Authors
Funding
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Hasenclver, L
Grant:
EP/L016710/1
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Webb, S
Grant:
EP/L015987/2
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Lienart, T
Grant:
EP/L505031/1
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Vollmer, S
Grant:
EP/K009850/1
+ Engineering and Physical Sciences Research Council
More from this funder
Funding agency for:
Teh, Y
Grant:
617071
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Bibliographic Details
- 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:
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1533-7928
- ISSN:
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1532-4435
- Source identifiers:
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738504
Item Description
- Keywords:
- Pubs id:
-
pubs:738504
- UUID:
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uuid:8963f732-32a9-4f37-9856-090390b0cec9
- Local pid:
- pubs:738504
- Deposit date:
- 2017-10-25
Terms of use
- Copyright holder:
- c 2017 Hasenclever, et al
- Copyright date:
- 2017
- Notes:
-
License: CC-BY 4.0, see https://creativecommons.org/licenses/by/4.0/. Attribution requirements are provided
at http://jmlr.org/papers/v18/16-478.html.
- Licence:
- CC Attribution (CC BY)
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