Multiarmed Bandits With Limited Expert Advice
Abstract
We solve the COLT 2013 open problem of SCB on minimizing regret in the setting of advice-efficient multiarmed bandits with expert advice. We give an algorithm for the setting of K arms and N experts out of which we are allowed to query and use only M experts' advices in each round, which has a regret bound of O\K, M\ NM T after T rounds. We also prove that any algorithm for this problem must have expected regret at least \K, M\ NMT, thus showing that our upper bound is nearly tight.
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