  We study random designs that minimize the asymptotic variance of a de-biased
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.
