Before revision:  We establish that under weak assumptions the estimator  concentrates in a 1 /root n neighborhood of the target with deviations controlled by the normal laws, and the estimator attains the semi-parametric efficiency bound in many cases. In particular, either the approximation to the regression function or the approximation to the Riesz representer can be "dense" as long as one of them is sufficiently "sparse". Our main results are non-asymptotic and imply asymptotic uniform validity over large classes of models\to  .
After revision:  We establish that under weak assumptions the estimator concentrates in a 1 /root n neighborhood of the target with deviations controlled by the Gaussian law, provided L/root(n)\to .