Design of c-optimal experiments for high-dimensional linear models

Abstract

We study randomized designs that minimize the asymptotic variance of a debiased lasso estimator when a large pool of unlabeled data is available but measuring the corresponding responses is costly. The optimal sampling distribution arises as the solution of a semidefinite program. The improvements in efficiency that result from these optimal designs are demonstrated via simulation experiments.