Continuous Diffusion for Mixed-Type Tabular Data

ICLR 2025 Conference Submission6164 Authors

Keywords: synthetic data generation, diffusion model, generative model, tabular data, mixed-type data

TL;DR: We propose a diffusion model for mixed-type tabular data that carefully balances generation of continuous and categorical features and show that accounting for feature heterogeneity in the design of adaptive noise schedules increases sample quality.

Abstract:

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.

Primary Area: generative models

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 6164