Supplementary materials - NeuMiss networks: differentiable programming for supervised learning with missing values

The last equality allows to conclude the proof. Assume that the data are generated via the linear model defined in equation (1) and satisfy Assumption 1. Additionally, assume that either Assumption 2 or Assumption 3 holds. Lemma 1 gives the general expression of the Bayes predictor for any data distribution and missing data mechanism. This concludes the proof according to Lemma 1. Assume that the data are generated via the linear model defined in equation (1) and satisfy Assumption 1 and Assumption 4. Let Here we establish an auxiliary result, controlling the convergence of Neumann iterates to the matrix inverse.