Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits

Feb-9-2026, 02:49:37 GMT–Neural Information Processing Systems

We consider the problem of regret minimization in non-parametric stochastic bandits. When the rewards are known to be bounded from above, there exists asymptotically optimal algorithms, with asymptotic regret depending on an infi-mum of Kullback-Leibler divergences (KL).

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Neural Information Processing Systems

Feb-9-2026, 02:49:37 GMT

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