Conference item
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 optimized with respect to an arbitrary subset of its variables.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 18.1MB, Terms of use)
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Authors
- Publisher:
- Neural Information Processing Systems Foundation
- Host title:
- NIPS 2016: 29th Annual Conference on Neural Information Processing Systems
- Journal:
- NIPS 2016 More from this journal
- Publication date:
- 2016-12-01
- Acceptance date:
- 2016-05-20
- Event location:
- Barcelona
- Event start date:
- 2016-12-05
- Event end date:
- 2016-12-10
- Pubs id:
-
pubs:664824
- UUID:
-
uuid:cf44b369-52b5-40fc-bc3d-b4e3f69bf86c
- Local pid:
-
pubs:664824
- Source identifiers:
-
664824
- Deposit date:
-
2016-12-09
- ARK identifier:
Terms of use
- Copyright date:
- 2016
- Notes:
- This article was presented at NIPS 2016: 29th Annual Conference on Neural Information Processing Systems (Barcelona, Spain: 5-10 December 2016).
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