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Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons
Neural Information Processing Systems
We study the sample complexity (i.e., the number of comparisons needed) bounds for actively ranking a set of n items from multi-wise comparisons. Here, a multiwise comparison takes m items as input and returns a (noisy) result about the best item (the winner feedback) or the order of these items (the full-ranking feedback). We consider two basic ranking problems: top-k items selection and full ranking. Unlike previous works that study ranking from multi-wise comparisons, in this paper, we dodblp:conf/nips/RenLS21 fatcat:yuborapfirc6fjwt5dj6ywaqoq