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Conference Papers Year : 2013

Information Complexity in Bandit Subset Selection


We consider the problem of efficiently exploring the arms of a stochastic bandit to identify the best subset of a specified size. Under the PAC and the fixed-budget formulations, we derive improved bounds by using KL-divergence-based confidence intervals. Whereas the application of a similar idea in the regret setting has yielded bounds in terms of the KL-divergence between the arms, our bounds in the pure-exploration setting involve the ``Chernoff information'' between the arms. In addition to introducing this novel quantity to the bandits literature, we contribute a comparison between strategies based on uniform and adaptive sampling for pure-exploration problems, finding evidence in favor of the latter.
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hal-02288406 , version 1 (14-09-2019)


  • HAL Id : hal-02288406 , version 1


Emilie Kaufmann, Shivaram Kalyanakrishnan. Information Complexity in Bandit Subset Selection. Conference On Learning Theory, Jun 2013, Princeton, United States. ⟨hal-02288406⟩
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