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Approximation Bounds for Inference using Cooperative Cut

2011

Conference Paper

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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.

Author(s): Jegelka, S. and Bilmes, J.
Pages: 577-584
Year: 2011
Month: July
Day: 0
Editors: Getoor, L. , T. Scheffer
Publisher: International Machine Learning Society

Department(s): Empirical Inference
Research Project(s): Optimization and Large Scale Learning
Bibtex Type: Conference Paper (inproceedings)

Address: Madison, WI, USA
Digital: 0
Event Name: 28th International Conference on Machine Learning (ICML 2011)
Event Place: Bellevue, WA, USA
ISBN: 978-1-450-30619-5

Links: PDF
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BibTex

@inproceedings{JegelkaB2011_2,
  title = {Approximation Bounds for Inference using Cooperative Cut},
  author = {Jegelka, S. and Bilmes, J.},
  pages = {577-584},
  editors = {Getoor, L. , T. Scheffer},
  publisher = {International Machine Learning Society},
  address = {Madison, WI, USA},
  month = jul,
  year = {2011},
  month_numeric = {7}
}