A covariance-adapting algorithm for semi-bandits achieves asymptotically tight regret bounds under a new sub-exponential distribution family, with direct application to sparse rewards.
Proof of Theorem 3 ProofConsiderAcontainingn/mdisjoint actionsA 1
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Covariance-adapting algorithm for semi-bandits with application to sparse rewards
A covariance-adapting algorithm for semi-bandits achieves asymptotically tight regret bounds under a new sub-exponential distribution family, with direct application to sparse rewards.