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  Approximation Bounds for Inference using Cooperative Cut

Jegelka, S., & Bilmes, J. (2011). Approximation Bounds for Inference using Cooperative Cut. In L. Getoor, & T. Scheffer (Eds.), 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, J, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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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: Published in print
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 Identifiers: 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):
Getoor, L, Editor
Scheffer, T, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 577 - 584 Identifier: ISBN: 978-1-450-30619-5