Tight Regret Bounds for Stochastic Combinatorial Semi-Bandits

JMLR Workshop and Conference Proceedings, pp. 535-543, 2015.

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Abstract:

A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we close the problem of computationally and sample efficient learning in st...More

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