Efficient Learning in Large-Scale Combinatorial Semi-Bandits

ICML, pp. 1113-1122, 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 combinatorial constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we consider efficient learning in large-scale combinatorial...More

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