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.
Benchmarking distribution shift in tabular data with tableshift
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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.