Conference item
Tractable uncertainty for structure learning
- Abstract:
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Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilistic circuits as the representation of our posterior belief. In contrast to sample-based posterior approximations, our representation can capture a much richer space of DAGs, while also being able to ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 1.1MB, Terms of use)
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- Publication website:
- https://proceedings.mlr.press/v162/wang22ad.html
Authors
Funding
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research
- Host title:
- Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
- Journal:
- Proceedings of Machine Learning Research More from this journal
- Volume:
- 162
- Pages:
- 23131-23150
- Publication date:
- 2022-07-01
- Acceptance date:
- 2022-05-16
- Event title:
- 39th International Conference on Machine Learning (ICML 2022)
- Event location:
- Baltimore, USA
- Event website:
- https://icml.cc/
- Event start date:
- 2022-07-17
- Event end date:
- 2022-07-23
Item Description
- Language:
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English
- Keywords:
- Pubs id:
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1280178
- Local pid:
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pubs:1280178
- Deposit date:
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2022-09-29
Terms of use
- Copyright holder:
- Wang et al
- Copyright date:
- 2022
- Rights statement:
- © 2022 by the author(s).
- Notes:
- This paper was presented at the 39th International Conference on Machine Learning (ICML 2022), 17th-23rd May 2022, Baltimore, USA.
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