  Score-based generative models (or diffusion models for short) have proven
successful for generating text and image data. However, the adaption of this
model family to tabular data of mixed-type has fallen short so far. In this
paper, we propose CDTD, a Continuous Diffusion model for mixed-type Tabular
Data. Specifically, we combine score matching and score interpolation to ensure
a common continuous noise distribution for both continuous and categorical
features alike. We counteract the high heterogeneity inherent to data of
mixed-type with distinct, adaptive noise schedules per feature or per data
type. The learnable noise schedules ensure optimally allocated model capacity
and balanced generative capability. We homogenize the data types further with
model-specific loss calibration and initialization schemes tailored to
mixed-type tabular data. Our experimental results show that CDTD consistently
outperforms state-of-the-art benchmark models, captures feature correlations
exceptionally well, and that heterogeneity in the noise schedule design boosts
the sample quality.
