<div class="csl-bib-body">
<div class="csl-entry">Peruvemba Ramaswamy, V., & Szeider, S. (2022). Learning Fast-Inference Bayesian Networks. In <i>Advances in Neural Information Processing Systems 34 (NeurIPS 2021)</i>. 35th conference on neural information processing systems (NeurIPS 2021), Unknown. https://doi.org/10.34726/4023</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/176903
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dc.identifier.uri
https://doi.org/10.34726/4023
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dc.description
Zählung der Konferenz weicht von Zählung des Konferenzbandes ab.
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dc.description.abstract
We propose new methods for learning Bayesian networks (BNs) that reliably support fast inference. We utilize maximum state space size as a more fine-grained measure for the BN's reasoning complexity than the standard treewidth measure, thereby accommodating the possibility that variables range over domains of different sizes. Our methods combine heuristic BN structure learning algorithms with the recently introduced MaxSAT-powered local improvement method (Peruvemba Ramaswamy and Szeider, AAAI'21). Our experiments show that our new learning methods produce BNs that support significantly faster exact probabilistic inference than BNs learned with treewidth bounds.
en
dc.description.sponsorship
FWF Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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dc.description.sponsorship
WWTF Wiener Wissenschafts-, Forschu und Technologiefonds
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dc.language.iso
en
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dc.relation.ispartofseries
NeurIPS Proceedings
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dc.rights.uri
http://rightsstatements.org/vocab/InC/1.0/
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dc.subject
Bayesian Networks Structure Learning
en
dc.subject
Exact Probabilistic Reasoning
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dc.subject
MaxSAT
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dc.subject
Propositional Satisfiability
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dc.title
Learning Fast-Inference Bayesian Networks
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dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.rights.license
Urheberrechtsschutz
de
dc.rights.license
In Copyright
en
dc.identifier.doi
10.34726/4023
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dc.relation.isbn
9781713845393
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dc.relation.grantno
P32441-N35
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dc.relation.grantno
ICT19-065
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
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tuw.container.volume
34
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tuw.peerreviewed
true
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tuw.book.ispartofseries
NeurIPS Proceedings
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tuw.project.title
SAT-Basierende lokale Verbesserungsmethoden
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tuw.project.title
Revealing and Utilizing the Hidden Structure for Solving Hard Problems in AI