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We consider the task of identifying the Copeland winner(s) in a duelingbandits problem with ternary feedback. This is an underexplored but practicallyrelevant variant of the conventional dueling bandits problem, in which, inaddition to strict preference between two arms, one may observe feedback in theform of an indifference. We provide a lower bound on the sample complexity forany learning algorithm finding the Copeland winner(s) with a fixed errorprobability. Moreover, we propose POCOWISTA, an algorithm with a samplecomplexity that almost matches this lower bound, and which shows excellentempirical performance, even for the conventional dueling bandits problem. Forthe case where the preference probabilities satisfy a specific type ofstochastic transitivity, we provide a refined version with an improved worstcase sample complexity.

Identifying Copeland Winners in Dueling Bandits with Indifferences