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|
|Date of revision:|
|Note:||View the original document on HAL open archive server: http://hal.archives-ouvertes.fr/hal-00362643/en/|
|Contact details of provider:|| Web page: http://hal.archives-ouvertes.fr/|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Mengel, Friederike, 2012.
"Learning across games,"
Games and Economic Behavior,
Elsevier, vol. 74(2), pages 601-619.
When requesting a correction, please mention this item's handle: RePEc:hal:wpaper:hal-00362643. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (CCSD)
If references are entirely missing, you can add them using this form.