Hannan consistency in on-line learning in case of unbounded losses under partial monitoring

Chamy Allenberg, Peter Auer, László Györfi, György Ottucsák

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Citations (Scopus)

Abstract

In this paper the sequential prediction problem with expert advice is considered when the loss is unbounded under partial monitoring scenarios. We deal with a wide class of the partial monitoring problems: the combination of the label efficient and multi-armed bandit problem, that is, where the algorithm is only informed about the performance of the chosen expert with probability ε ≤ 1. For bounded losses an algorithm is given whose expected regret scales with the square root of the loss of the best expert. For unbounded losses we prove that Hannan consistency can be achieved, depending on the growth rate of the average squared losses of the experts.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings
PublisherSpringer Verlag
Pages229-243
Number of pages15
ISBN (Print)3540466495, 9783540466499
Publication statusPublished - Jan 1 2006
Event17th International Conference on Algorithmic Learning Theory, ALT 2006 - Barcelona, Spain
Duration: Oct 7 2006Oct 10 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4264 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other17th International Conference on Algorithmic Learning Theory, ALT 2006
CountrySpain
CityBarcelona
Period10/7/0610/10/06

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Allenberg, C., Auer, P., Györfi, L., & Ottucsák, G. (2006). Hannan consistency in on-line learning in case of unbounded losses under partial monitoring. In Algorithmic Learning Theory - 17th International Conference, ALT 2006, Proceedings (pp. 229-243). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4264 LNAI). Springer Verlag.