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Linear Multi-Resource Allocation with Semi-Bandit Feedback
Tor Lattimore · Yacov Crammer · Csaba Szepesvari

Wed Dec 09 04:00 PM -- 08:59 PM (PST) @ 210 C #90

We study an idealised sequential resource allocation problem. In each time step the learner chooses an allocation of several resource types between a number of tasks. Assigning more resources to a task increases the probability that it is completed. The problem is challenging because the alignment of the tasks to the resource types is unknown and the feedback is noisy. Our main contribution is the new setting and an algorithm with nearly-optimal regret analysis. Along the way we draw connections to the problem of minimising regret for stochastic linear bandits with heteroscedastic noise. We also present some new results for stochastic linear bandits on the hypercube that significantly out-performs existing work, especially in the sparse case.

Author Information

Tor Lattimore (University of Alberta)
Yacov Crammer (Technion)
Csaba Szepesvari (University of Alberta)

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