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  Online submodular minimization for combinatorial structures

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

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http://www.icml-2011.org/ (Table of contents)
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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: 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
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 Table of Contents: -
 Rev. Type: -
 Identifiers: 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):
Getoor, L, Editor
Scheffer, T, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 345 - 352 Identifier: ISBN: 978-1-450-30619-5