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  • Publication . Article . Preprint . 2013 . Embargo End Date: 01 Jan 2013
    Open Access
    Authors: 
    Zoghi, Masrour; Whiteson, Shimon; Munos, Remi; de Rijke, Maarten;

    doi: 10.48550/arxiv.1312.3393

    Publisher: arXiv
    Country: Netherlands
    Project: NWO | Modeling and Learning fro... (8686), EC | COMPLACS (270327), NWO | Building Rich Links to En... (2300153702), EC | LIMOSINE (288024), NWO | Digging archaeology data:... (25409), NWO | SPuDisc: Searching Public... (2300176811), NWO | Semantic Search in E-Disc... (7999)

    This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art. Comment: 13 pages, 6 figures

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    Average influence In bottom 99%
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Advanced search in Research products
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The following results are related to Digital Humanities and Cultural Heritage. Are you interested to view more results? Visit OpenAIRE - Explore.
1 Research products, page 1 of 1
  • Publication . Article . Preprint . 2013 . Embargo End Date: 01 Jan 2013
    Open Access
    Authors: 
    Zoghi, Masrour; Whiteson, Shimon; Munos, Remi; de Rijke, Maarten;

    doi: 10.48550/arxiv.1312.3393

    Publisher: arXiv
    Country: Netherlands
    Project: NWO | Modeling and Learning fro... (8686), EC | COMPLACS (270327), NWO | Building Rich Links to En... (2300153702), EC | LIMOSINE (288024), NWO | Digging archaeology data:... (25409), NWO | SPuDisc: Searching Public... (2300176811), NWO | Semantic Search in E-Disc... (7999)

    This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art. Comment: 13 pages, 6 figures

    Average popularity
    Average popularity In bottom 99%
    Popularity: Citation-based measure reflecting the current impact.
    Average influence
    Average influence In bottom 99%
    Influence: Citation-based measure reflecting the total impact.
    addAdd to  ORCID orcid