  Tabular data plays a crucial role in various domains but often suffers from
missing values, thereby curtailing its potential utility. Traditional
imputation techniques frequently yield suboptimal results and impose
substantial computational burdens, leading to inaccuracies in subsequent
modeling tasks. To address these challenges, we propose DiffImpute, a novel
Denoising Diffusion Probabilistic Model (DDPM). Specifically, DiffImpute is
trained on complete tabular datasets, ensuring that it can produce credible
imputations for missing entries without undermining the authenticity of the
existing data. Innovatively, it can be applied to various settings of Missing
Completely At Random (MCAR) and Missing At Random (MAR). To effectively handle
the tabular features in DDPM, we tailor four tabular denoising networks,
spanning MLP, ResNet, Transformer, and U-Net. We also propose Harmonization to
enhance coherence between observed and imputed data by infusing the data back
and denoising them multiple times during the sampling stage. To enable
efficient inference while maintaining imputation performance, we propose a
refined non-Markovian sampling process that works along with Harmonization.
Empirical evaluations on seven diverse datasets underscore the prowess of
DiffImpute. Specifically, when paired with the Transformer as the denoising
network, it consistently outperforms its competitors, boasting an average
ranking of 1.7 and the most minimal standard deviation. In contrast, the next
best method lags with a ranking of 2.8 and a standard deviation of 0.9. The
code is available at https://github.com/Dendiiiii/DiffImpute.
