2026
Towards Synthesizing High-Dimensional Tabular Data with Limited Samples
AAAI 2026technical
Diffusion-based tabular data synthesis models have yielded promising results. However, we observe that when the data dimensionality increases, existing models tend to degenerate and may perform even worse than simpler, non-diffusion-based models. This is because limited training samples in high-dime