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

arXiv:2607.23348 · stat.ML, cs.LG · Submitted 2026-07-25 · Read on arXiv

Yuefei Shen, Xiaotong Shen

stat.ML, cs.LG

Submitted: 2026-07-25

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Flow Matching — 85× in this paper · explained in 22 episodes
• Fidelity — 42× in this paper · explained in 2 episodes
• Generative Model — 24× in this paper · explained in 2 episodes
• Benchmark — 23× in this paper · explained in 2 episodes
• Generative Models — 16× in this paper · explained in 3 episodes
• Generative Modeling — 12× in this paper · explained in 3 episodes
• Parameterization — 10× in this paper · explained in 2 episodes
• AUC — 9× in this paper · explained in 2 episodes
• Diffusion Model — 9× in this paper · explained in 10 episodes
• Diffusion Models — 9× in this paper · explained in 9 episodes
• Generator — 9× in this paper · explained in 2 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Fisher Information — 6× in this paper · explained in 4 episodes
• Flow Matching Model — 5× in this paper · explained in 3 episodes
• Kullback–Leibler Divergence — 4× in this paper · explained in 2 episodes
• Optimal Transport — 4× in this paper · explained in 5 episodes
• Decoder — 3× in this paper · explained in 2 episodes
• Training Objective — 3× in this paper · explained in 3 episodes
• Calibration — 2× in this paper · explained in 9 episodes
• International Conference on Learning Representations — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Class Imbalance — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Flow Matching (FM) — 1× in this paper · explained in 4 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 1× in this paper
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Localization — 1× in this paper · explained in 2 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Training Objectives — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 6 episodes

Sources

• Flow matching achieves almost minimax optimal convergence
• Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features
• Categorical Flow Maps
• Simplex-to-Euclidean Bijections for Categorical Flow Matching

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