Multi-armed bandits on implicit metric spaces

NeurIPS 2011 Aleksandrs Slivkins

The multi-armed bandit (MAB) setting is a useful abstraction of many online learning tasks which focuses on the trade-off between exploration and exploitation. In this setting, an online algorithm has a fixed set of alternatives ("arms"), and in each round it selects one arm and then observes the corresponding reward... (read more)

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