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International Conference on Algorithmic Learning Theory

ALT 2013: Algorithmic Learning Theory pp 22–32Cite as

Efficient Algorithms for Combinatorial Online Prediction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8139))

Abstract

We study online linear optimization problems over concept classes which are defined in some combinatorial ways. Typically, those concept classes contain finite but exponentially many concepts and hence the complexity issue arises. In this paper, we survey some recent results on universal and efficient implementations of low-regret algorithmic frameworks such as Follow the Regularized Leader (FTRL) and Follow the Perturbed Leader (FPL).

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Author information

Authors and Affiliations

  1. Department of Informatics, Kyushu University, Japan

    Eiji Takimoto & Kohei Hatano

Authors
  1. Eiji Takimoto
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  2. Kohei Hatano
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Editor information

Editors and Affiliations

  1. National University of Singapore, Republic of Singapore

    Sanjay Jain  & Frank Stephan  & 

  2. Inria Lille - Nord Europe, Villeneuve d’Ascq, France

    Rémi Munos

  3. Hokkaido University, Sapporo, Japan

    Thomas Zeugmann

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Takimoto, E., Hatano, K. (2013). Efficient Algorithms for Combinatorial Online Prediction. In: Jain, S., Munos, R., Stephan, F., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2013. Lecture Notes in Computer Science(), vol 8139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40935-6_3

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  • DOI: https://doi.org/10.1007/978-3-642-40935-6_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40934-9

  • Online ISBN: 978-3-642-40935-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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