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We provide adaptive inference methods, based on $\ell_1$ regularization, forregular (semi-parametric) and non-regular (nonparametric) linear functionals ofthe conditional expectation function. Examples of regular functionals includeaverage treatment effects, policy effects, and derivatives. Examples ofnon-regular functionals include average treatment effects, policy effects, andderivatives conditional on a covariate subvector fixed at a point. We constructa Neyman orthogonal equation for the target parameter that is approximatelyinvariant to small perturbations of the nuisance parameters. To achieve thisproperty, we include the Riesz representer for the functional as an additionalnuisance parameter. Our analysis yields weak ``double sparsity robustness'':either the approximation to the regression or the approximation to therepresenter can be ``completely dense'' as long as the other is sufficiently``sparse''. Our main results are non-asymptotic and imply asymptotic uniformvalidity over large classes of models, translating into honest confidence bandsfor both global and local parameters.

De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers