High dimensional regression and matrix estimation without tuning parameters
arXiv:1510.07294
Abstract
A general theory for Gaussian mean estimation that automatically adapts to unknown sparsity under arbitrary norms is proposed. The theory is applied to produce adaptively minimax rate-optimal estimators in high dimensional regression and matrix estimation that involve no tuning parameters.
23 pages, 1 figure. Minor corrections in this revision