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Online Multi-task Learning with Hard Constraints

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  • Gabor Lugosi

    ()
    (ICREA - Institució Catalana de Recerca i Estudis Avançats [Barcelona] - ICREA - Universitat de Barcelona - Fundació Catalana per a la Recerca i la Innovació (FCRI))

  • Omiros Papaspiliopoulos

    ()
    (ICREA - Institució Catalana de Recerca i Estudis Avançats [Barcelona] - ICREA - Universitat de Barcelona - Fundació Catalana per a la Recerca i la Innovació (FCRI))

  • Gilles Stoltz

    ()
    (DMA - Département de Mathématiques et Applications - CNRS : UMR8553 - Ecole Normale Supérieure de Paris - ENS Paris, GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - GROUPE HEC - CNRS : UMR2959)

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    Abstract

    We discuss multi-task online learning when a decision maker has to deal simultaneously with M tasks. The tasks are related, which is modeled by imposing that the M-tuple of actions taken by the decision maker needs to satisfy certain constraints. We give natural examples of such restrictions and then discuss a general class of tractable constraints, for which we introduce computationally efficient ways of selecting actions, essentially by reducing to an on-line shortest path problem. We briefly discuss ``tracking'' and ``bandit'' versions of the problem and extend the model in various ways, including non-additive global losses and uncountably infinite sets of tasks.

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    File URL: http://hal.archives-ouvertes.fr/docs/00/36/95/89/PDF/LugPapSto-MultiTask.pdf
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    Bibliographic Info

    Paper provided by HAL in its series Working Papers with number hal-00362643.

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    Date of creation: 13 Feb 2009
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    Handle: RePEc:hal:wpaper:hal-00362643

    Note: View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00362643/en/
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    1. Mengel, Friederike, 2012. "Learning across games," Games and Economic Behavior, Elsevier, vol. 74(2), pages 601-619.
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