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Semi-Bandit Learning for Monotone Stochastic Optimization
Stochastic optimization is a widely used approach for optimization under uncertainty, where uncertain input parameters are modeled by random variables.
An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem
}$ We answer this in the affirmative $\textit{under the Condorcet condition}$, a standard setting of the $K$-armed dueling bandit problem.
Batched Dueling Bandits
The $K$-armed dueling bandit problem, where the feedback is in the form of noisy pairwise comparisons, has been widely studied.
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