  Diffusion model has become a main paradigm for synthetic data generation in
many subfields of modern machine learning, including computer vision, language
model, or speech synthesis. In this paper, we leverage the power of diffusion
model for generating synthetic tabular data. The heterogeneous features in
tabular data have been main obstacles in tabular data synthesis, and we tackle
this problem by employing the auto-encoder architecture. When compared with the
state-of-the-art tabular synthesizers, the resulting synthetic tables from our
model show nice statistical fidelities to the real data, and perform well in
downstream tasks for machine learning utilities. We conducted the experiments
over $15$ publicly available datasets. Notably, our model adeptly captures the
correlations among features, which has been a long-standing challenge in
tabular data synthesis. Our code is available at
https://github.com/UCLA-Trustworthy-AI-Lab/AutoDiffusion.
