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Preselection Bandits
Viktor Bengs · Eyke Hüllermeier

Thu Jul 16 01:00 PM -- 01:45 PM & Fri Jul 17 02:00 AM -- 02:45 AM (PDT) @ Virtual #None

In this paper, we introduce the Preselection Bandit problem, in which the learner preselects a subset of arms (choice alternatives) for a user, which then chooses the final arm from this subset. The learner is not aware of the user's preferences, but can learn them from observed choices. In our concrete setting, we allow these choices to be stochastic and model the user's actions by means of the Plackett-Luce model. The learner's main task is to preselect subsets that eventually lead to highly preferred choices. To formalize this goal, we introduce a reasonable notion of regret and derive lower bounds on the expected regret. Moreover, we propose algorithms for which the upper bound on expected regret matches the lower bound up to a logarithmic term of the time horizon.

Author Information

Viktor Bengs (University of Paderborn)
Eyke Hüllermeier (Paderborn University)

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