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

arXiv:2309.09968v3 Announce Type: replace
Abstract: 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 …

arxiv cs.lg data diffusion flow gradient tabular tabular data trees type via