  Denoising diffusion probabilistic models are currently becoming the leading
paradigm of generative modeling for many important data modalities. Being the
most prevalent in the computer vision community, diffusion models have also
recently gained some attention in other domains, including speech, NLP, and
graph-like data. In this work, we investigate if the framework of diffusion
models can be advantageous for general tabular problems, where datapoints are
typically represented by vectors of heterogeneous features. The inherent
heterogeneity of tabular data makes it quite challenging for accurate modeling,
since the individual features can be of completely different nature, i.e., some
of them can be continuous and some of them can be discrete. To address such
data types, we introduce TabDDPM -- a diffusion model that can be universally
applied to any tabular dataset and handles any type of feature. We extensively
evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority
over existing GAN/VAE alternatives, which is consistent with the advantage of
diffusion models in other fields. Additionally, we show that TabDDPM is
eligible for privacy-oriented setups, where the original datapoints cannot be
publicly shared.
