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 vecto...
| Authors | Yuefei Shen, Xiaotong Shen |
|---|---|
| Primary category | stat.ML |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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