NeuMiss networks: differentiable programming for supervised learning with missing values
NeuMiss networks: differentiable programming for supervised learning with missing values
The presence of missing values makes supervised learning much more challenging. Indeed, previous work has shown that even when the response is a linear function of the complete data, the optimal predictor is a complex function of the observed entries and the missingness indicator. As a result, the computational or …