On the Sample Complexity of Rank Regression from Pairwise Comparisons
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We consider a rank regression setting, in which a dataset of N samples with features in ℝ^d is ranked by an oracle via M pairwise comparisons. Specifically, there exists a latent total ordering of the samples; when presented with a pair of samples, a noisy oracle identifies the one ranked higher with respect to the underlying total ordering. A learner observes a dataset of such comparisons and wishes to regress sample ranks from their features. We show that to learn the model parameters with ϵ > 0 accuracy, it suffices to conduct M ∈Ω(dNlog^3 N/ϵ^2) comparisons uniformly at random when N is Ω(d/ϵ^2).
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