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  Online submodular minimization for combinatorial structures

Jegelka, S., & Bilmes, J. (2011). Online submodular minimization for combinatorial structures. In 28th International Conference on Machine Learning (ICML 2011) (pp. 345-352). Madison, WI, USA: International Machine Learning Society.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB26-5 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BB27-3
Genre: Conference Paper

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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: Most results for online decision problems with structured concepts, such as trees or cuts, assume linear costs. In many settings, however, nonlinear costs are more realistic. Owing to their non-separability, these lead to much harder optimization problems. Going beyond linearity, we address online approximation algorithms for structured concepts that allow the cost to be submodular, i.e., nonseparable. In particular, we show regret bounds for three Hannan-consistent strategies that capture different settings. Our results also tighten a regret bound for unconstrained online submodular minimization.

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 Dates: 2011-07
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: -
 Identifiers: ISBN: 978-1-450-30619-5
URI: http://www.icml-2011.org/
BibTex Citekey: JegelkaB2011_3
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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):
Affiliations:
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 345 - 352 Identifier: -