Exponential Family Variational Flow Matching for Tabular Data Generation
Andrés Guzmán-Cordero, Floor Eijkelboom, Jan-Willem van de Meent
Abstract
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.
BibTeX
@inproceedings{
guzman-cordero2025exponential,
title={Exponential Family Variational Flow Matching for Tabular Data Generation},
author={Andr{\'e}s Guzm{\'a}n-Cordero and Floor Eijkelboom and Jan-Willem van de Meent},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=kjtvCSkSsy}
}