A regularization framework trains trees, LASSO, and ensembles to avoid relying on features that are missing at test time, cutting missingness reliance sharply with small AUROC losses.
Learning sparse classifiers: Continuous and mixed integer optimization perspectives
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Prediction Models That Learn to Avoid Missing Values
A regularization framework trains trees, LASSO, and ensembles to avoid relying on features that are missing at test time, cutting missingness reliance sharply with small AUROC losses.