  Tabular data is hard to acquire and is subject to missing values. This paper
introduces a novel approach for generating and imputing mixed-type (continuous
and categorical) tabular data utilizing score-based diffusion and conditional
flow matching. In contrast to prior methods that rely on neural networks to
learn the score function or the vector field, we adopt XGBoost, a widely used
Gradient-Boosted Tree (GBT) technique. To test our method, we build one of the
most extensive benchmarks for tabular data generation and imputation,
containing 27 diverse datasets and 9 metrics. Through empirical evaluation
across the benchmark, we demonstrate that our approach outperforms
deep-learning generation methods in data generation tasks and remains
competitive in data imputation. Notably, it can be trained in parallel using
CPUs without requiring a GPU. Our Python and R code is available at
https://github.com/SamsungSAILMontreal/ForestDiffusion.
