Abstract:We consider PAC-learning a good item from k-subsetwise feedback information sampled from a Plackett-Luce probability model, with instance-dependent sample complexity performance. In the setting where subsets of a fixed size can be tested and top-ranked feedback is made available to the learner, we give an algorithm with optimal instance-dependent sample complexity, for PAC best arm identification, of Obeing the Plackett-Luce parameter gap between the best and the i th best item, andis the sum of the Plackett-L… Show more
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