  Score-based generative models (or diffusion models for short) have proven
successful across many domains in generating text and image data. However, the
consideration of mixed-type tabular data with this model family has fallen
short so far. Existing research mainly combines different diffusion processes
without explicitly accounting for the feature heterogeneity inherent to tabular
data. In this paper, we combine score matching and score interpolation to
ensure a common type of continuous noise distribution that affects both
continuous and categorical features alike. Further, we investigate the impact
of distinct noise schedules per feature or per data type. We allow for
adaptive, learnable noise schedules to ensure optimally allocated model
capacity and balanced generative capability. Results show that our model
consistently outperforms state-of-the-art benchmark models and that accounting
for heterogeneity within the noise schedule design boosts the sample quality.

Machine Learning;Machine Learning