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  Approximation Bounds for Inference using Cooperative Cut

Jegelka, S., & Bilmes, J. (2011). Approximation Bounds for Inference using Cooperative Cut. In 28th International Conference on Machine Learning (ICML 2011) (pp. 577-584). Madison, WI, USA: International Machine Learning Society.

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 Creators:
Jegelka, S1, Author           
Bilmes, J1, Author           
Getoor T. Scheffer, L., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: We analyze a family of probability distributions that are characterized by an embedded combinatorial structure. This family includes models having arbitrary treewidth and arbitrary sized factors. Unlike general models with such freedom, where the “most probable explanation” (MPE) problem is inapproximable, the combinatorial structure within our model, in particular the indirect use of submodularity, leads to several MPE algorithms that all have approximation guarantees.

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 Dates: 2011-07
 Publication Status: Issued
 Pages: -
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 Rev. Type: -
 Identifiers: ISBN: 978-1-450-30619-5
URI: http://www.icml-2011.org/
BibTex Citekey: JegelkaB2011_2
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Title: 28th International Conference on Machine Learning (ICML 2011)
Place of Event: Bellevue, WA, USA
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Title: 28th International Conference on Machine Learning (ICML 2011)
Source Genre: Proceedings
 Creator(s):
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Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 577 - 584 Identifier: -