A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model

25 Jul 2017Xi ChenYuanzhi LiJieming Mao

We study the active learning problem of top-$k$ ranking from multi-wise comparisons under the popular multinomial logit model. Our goal is to identify the top-$k$ items with high probability by adaptively querying sets for comparisons and observing the noisy output of the most preferred item from each comparison... (read more)

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