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Tabular data is hard to acquire and is subject to missing values. This paperproposes a novel approach to generate and impute mixed-type (continuous andcategorical) tabular data using score-based diffusion and conditional flowmatching. Contrary to previous work that relies on neural networks to learn thescore function or the vector field, we instead rely on XGBoost, a popularGradient-Boosted Tree (GBT) method. We empirically show on 27 differentdatasets that our approach i) generates highly realistic synthetic data whenthe training dataset is either clean or tainted by missing data and ii)generates diverse plausible data imputations. Furthermore, our methodoutperforms deep-learning generation methods on data generation and iscompetitive on data imputation. Finally, it can be trained in parallel usingCPUs without the need for a GPU. To make it easily accessible, we release ourcode through a Python library and an R package.

Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees