While denoising diffusion and flow matching have driven major advances in generative modeling, their application to tabular data remains limited, despite its ubiquity in real-world applications. To this end, we develop TabbyFlow, a variational Flow Matching (VFM) method for tabular data generation. To apply VFM to data with mixed continuous and discrete features, we introduce Exponential Family Variational Flow Matching (EF-VFM), which represents heterogeneous data types using a general exponential family distribution. We hereby obtain an efficient, data-driven objective based on moment matching, enabling principled learning of probability paths over mixed continuous and discrete variables. We also establish a connection between variational flow matching and generalized flow matching objectives based on Bregman divergences. Evaluation on tabular data benchmarks demonstrates state-of-the-art performance compared to baselines.
@article{arxiv.2506.05940,
title = {Exponential Family Variational Flow Matching for Tabular Data Generation},
author = {Andrés Guzmán-Cordero and Floor Eijkelboom and Jan-Willem van de Meent},
journal= {arXiv preprint arXiv:2506.05940},
year = {2025}
}
Comments
14 pages, 1 figure, and 9 tables; To be published in the Proceedings of the Forty-Second International Conference on Machine Learning