Logit-Coordinate Generative Models for Mixed Continuous-Categorical Tabular Data

Mixed continuous--categorical data pose a representation problem for continuous generative models.

Flow Matching and Gaussian diffusion operate in Euclidean spaces, whereas categorical laws lie on probability simplices and may be highly imbalanced.

We study a logit-coordinate framework that encodes categorical variables as smoothed natural parameters and combines them with transformed numerical variables.

This yields common formulations of Logit Flow Matching and Logit Diffusion.

We introduce a mixed-distribution discrepancy separating categorical marginal error from conditional continuous Wasserstein error, and derive stability bounds and imbalance-aware nonparametric rates linking vector-field or drift error to decoded mixed-distribution error.

Controlled simulations show that scaled-logit coordinates improve or match one-hot coordinates, especially under severe rare-cell imbalance.

Across four real-data benchmarks and ten splits per dataset, Logit FM improves the primary distributional metrics on three datasets and is comparable on Churn2; Block-Conditional Logit FM consistently improves the flat model; and Logit Diffusion generally improves over or matches One-Hot Diffusion.