  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.
