  Ridge estimators regularize the squared Euclidean lengths of parameters. Such
estimators are mathematically and computationally attractive but involve tuning
parameters that can be difficult to calibrate. In this paper, we show that
ridge estimators can be modified such that tuning parameters can be avoided
altogether. We also show that these modified versions can improve on the
empirical prediction accuracies of standard ridge estimators combined with
cross-validation, and we provide first theoretical guarantees.

Methodology;Applications;Computation;Machine Learning