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In the paper we consider the ranking problem that is popular in the machine learning community. The goal is to predict or to guess the ordering between objects on the basis of their observed features. We focus on ranking estimators that are obtained by minimization of an empirical risk with a convex loss function. We pay special attention to “large” families of ranking rules that algorithms work with,...
We investigate properties of estimators obtained by minimization of U-processes with the Lasso penalty in the high-dimensional setting. Our attention is focused on the ranking problem that is popular in machine learning. It is related to guessing the ordering between objects on the basis of their observed predictors. We prove the oracle inequality for the excess risk of the considered estimator as...
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