NeuMiss networks tackle supervised learning with missing values, offering efficient and robust predictions.
problem Challenges in supervised learning with missing values, especially when the response is a linear function of the complete data.
method Derive analytical form of optimal predictor under linearity assumption and various missing data mechanisms. Propose NeuMiss networks using multiplication by missingness indicator.
result Upper bound on Bayes risk and good predictive accuracy with independent complexity of missing data patterns.