Aggregation and sparsity via l1 penalized least squares

This paper shows that near optimal rates of aggregation and adaptation to unknown sparsity can be simultaneously achieved via, penalized least squares in a nonparametric regression setting. The main tool is a novel oracle inequality on the sum between the empirical squared loss of the penalized least squares estimate and a term reflecting the sparsity of the unknown regression function.