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Approximation bounds for inference using cooperative cut

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Jegelka,  S.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Bilmes,  J.
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Citation

Jegelka, S., & Bilmes, J. (2011). Approximation bounds for inference using cooperative cut. In 28th International Conference on Machine Learning (ICML 2011) (pp. 577-584).


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-75BB-2
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.