Online Multi-task Learning with Hard Constraints

Details Online Multi-task Learning with Hard Constraints Online Multi-task Learning with Hard Constraints

2009-02-20

arxiv.org/abs/0902.3526v2

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Machine Learning

Scientific paper

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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