  We present ReMasker, a new method of imputing missing values in tabular data
by extending the masked autoencoding framework. Compared with prior work,
ReMasker is both simple -- besides the missing values (i.e., naturally masked),
we randomly ``re-mask'' another set of values, optimize the autoencoder by
reconstructing this re-masked set, and apply the trained model to predict the
missing values; and effective -- with extensive evaluation on benchmark
datasets, we show that ReMasker performs on par with or outperforms
state-of-the-art methods in terms of both imputation fidelity and utility under
various missingness settings, while its performance advantage often increases
with the ratio of missing data. We further explore theoretical justification
for its effectiveness, showing that ReMasker tends to learn
missingness-invariant representations of tabular data. Our findings indicate
that masked modeling represents a promising direction for further research on
tabular data imputation. The code is publicly available.
