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Bayesian Optimization for Probabilistic Programs

Abstract:

We outline a general purpose framework for black-box marginal maximum a pos- teriori estimation of probabilistic program variables using Bayesian optimization with Gaussian processes. We introduce the concept of an optimization query, whereby a probabilistic program returns an infinite lazy sequence of increasingly optimal estimates, and explain how a general purpose program transformation would allow the evidence of any probabilistic program, and therefore any graphical model, to be optimize...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Oxford college:
Exeter College
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
Publisher:
Neural Information Processing Systems Foundation Publisher's website
Journal:
NIPS 2016 Journal website
Host title:
NIPS 2016: 29th Annual Conference on Neural Information Processing Systems
Publication date:
2016-12-01
Acceptance date:
2016-05-20
Event location:
Barcelona
Event start date:
2016-12-05T00:00:00Z
Event end date:
2016-12-10T00:00:00Z
Source identifiers:
664824
Pubs id:
pubs:664824
UUID:
uuid:cf44b369-52b5-40fc-bc3d-b4e3f69bf86c
Local pid:
pubs:664824
Deposit date:
2016-12-09

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