  We investigate properties of estimators obtained by minimization of
U-processes with the Lasso penalty in high-dimensional settings. 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 well as the bound for the l1 distance between the
estimator and the oracle.
