Online allocation and homogeneous partitioning for piecewise constant mean-approximation

NeurIPS 2012 Alexandra CarpentierOdalric-Ambrym Maillard

In the setting of active learning for the multi-armed bandit, where the goal of a learner is to estimate with equal precision the mean of a finite number of arms, recent results show that it is possible to derive strategies based on finite-time confidence bounds that are competitive with the best possible strategy. We here consider an extension of this problem to the case when the arms are the cells of a finite partition P of a continuous sampling space X \subset \Real^d... (read more)

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