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Online Multi-task Learning with Hard Constraints


  • Gabor Lugosi

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

  • Omiros Papaspiliopoulos

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

  • Gilles Stoltz

    () (DMA - Département de Mathématiques et Applications - ENS Paris - École normale supérieure - Paris - CNRS - Centre National de la Recherche Scientifique, GREGH - Groupement de Recherche et d'Etudes en Gestion à HEC - HEC Paris - Ecole des Hautes Etudes Commerciales - CNRS - Centre National de la Recherche Scientifique)


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.

Suggested Citation

  • Gabor Lugosi & Omiros Papaspiliopoulos & Gilles Stoltz, 2009. "Online Multi-task Learning with Hard Constraints," Working Papers hal-00362643, HAL.
  • Handle: RePEc:hal:wpaper:hal-00362643
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    References listed on IDEAS

    1. Mengel, Friederike, 2012. "Learning across games," Games and Economic Behavior, Elsevier, vol. 74(2), pages 601-619.
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