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

Alexia Jolicoeur-Martineau · Kilian Fatras · Tal Kachman

MR1 & MR2 - Number 142
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Fri 3 May 8 a.m. PDT — 8:30 a.m. PDT


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 \url{}.

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