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.
Exploration-exploitation trade-off using variance estimates in multi-armed bandits.Theoretical Computer Science, 410:1876–1902
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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.