A training objective built on mutual-information robustness conditions plus extra random masking improves tabular model accuracy under missingness shifts between train and test, with gains also in fully observed settings.
Ehrtemporalvariability: delineating temporal data-set shifts in electronic health records
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MIRRAMS: Learning Robust Tabular Models under Unseen Missingness Shifts
A training objective built on mutual-information robustness conditions plus extra random masking improves tabular model accuracy under missingness shifts between train and test, with gains also in fully observed settings.