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Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space

Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space

This video was recorded at Video Journal of Machine Learning Abstracts - Volume 3. This paper is concerned with the statistical consistency of ranking methods. Recently, it was proven that many commonly used pairwise ranking methods are inconsistent with the weighted pairwise disagreement loss (WPDL), which can be viewed as the true loss of ranking, even in a low-noise setting. This result is interesting but also surprising, given that the pairwise ranking methods have been shown very effective in practice. In this paper, we argue that the aforementioned result might not be conclusive, depending on what kind of assumptions are used. We give a new assumption that the labels of objects to rank lie in a rank-differentiable probability space (RDPS), and prove that the pairwise ranking methods... Show More

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