Online Multi-task Learning with Hard Constraints
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.
|Date of creation:||13 Feb 2009|
|Note:||View the original document on HAL open archive server: https://hal.archives-ouvertes.fr/hal-00362643v2|
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- Mengel, Friederike, 2012.
"Learning across games,"
Games and Economic Behavior,
Elsevier, vol. 74(2), pages 601-619.
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