SupplementaryMaterial

R φqφ(z)dz = 0. Thus, the gradient of the log-variance loss becomes equaltothegradientofthe KL divergence. Therefore, for large enough D, the condition from Proposition 3 (see Eq. 19), is fulfilled and the statement follows immediately. This result isexpected to extend to the multivariate cases as well. For all the experiments listed in the main text, we use the VarGrad estimator for the gradients of the logistic regression models. VarGrad achieves considerable variance reduction over the adaptive (RELAX) and non-adaptive (ControlledReinforce)model-agnosticestimators.