Before revision:  Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, ), plugging in a first-stage estimate , computed on a hold-out sample. We define a population loss to be (Neyman) orthogonal if the gradient of the loss with respect to \theta, has pathwise derivative with respect to g equal to zero, when evaluated at the true parameter and nuisance component.
After revision:  Our approach is based on estimating \theta via an \ell_1-regularized minimization of a sample analog of L_S(\theta, ), plugging in a first-stage estimate into the CMR, we construct the M-estimator loss function for the target parameter so that its gradient is insensitive (formally, Neyman-orthogonal) with respect to g equal to zero, when evaluated at the true parameter and nuisance component.