Tabular data is hard to acquire and is subject to missing values. This paper proposes a novel approach to generate and impute mixed-type (continuous and categorical) tabular data using score-based diffusion and conditional flow matching. Contrary to previous work that relies on neural networks as function approximators, we instead utilize XGBoost, a popular Gradient-Boosted Tree (GBT) method. In addition to being elegant, we empirically show on various datasets that our method i) generates highly realistic synthetic data when the training dataset is either clean or tainted by missing data and ii) generates diverse plausible data imputations. Our method often outperforms deep-learning generation methods and can trained in parallel using CPUs without the need for a GPU. To make it easily accessible, we release our code through a Python library on PyPI and an R package on CRAN.

翻译：表格数据难以获取且常存在缺失值。本文提出了一种新方法，利用基于分数的扩散和条件流匹配来生成和插补混合类型（连续和分类）的表格数据。与先前依赖神经网络作为函数逼近器的工作不同，我们转而采用XGBoost——一种流行的梯度提升树方法。除了方法简洁优雅外，我们在多个数据集上的实验表明：i) 当训练数据集完整或含有缺失数据时，该方法能生成高度真实的合成数据；ii) 能生成多样化的合理数据插补结果。该方法通常在性能上超越深度学习方法，且无需GPU即可在CPU上并行训练。为便于使用，我们通过PyPI发布Python库、在CRAN发布R包提供开源代码。