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NeuMiss networks: differentiable programming for supervised learning with missing values.
We provide an upper bound on the Bayes risk of NeuMiss networks, and show that they have good predictive accuracy with both a number of parameters and a computational complexity independent of the number of missing data patterns.
Debiasing Averaged Stochastic Gradient Descent to handle missing values
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.
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