Synthesizing high-quality tabular data is an important topic in many data science tasks, ranging from dataset augmentation to privacy protection. However, developing expressive generative models for tabular data is challenging due to its inherent heterogeneous data types, complex inter-correlations, and intricate column-wise distributions. In this paper, we introduce TabDiff, a joint diffusion framework that models all multi-modal distributions of tabular data in one model. Our key innovation is the development of a joint continuous-time diffusion process for numerical and categorical data, where we propose feature-wise learnable diffusion processes to counter the high disparity of different feature distributions. TabDiff is parameterized by a transformer handling different input types, and the entire framework can be efficiently optimized in an end-to-end fashion. We further introduce a multi-modal stochastic sampler to automatically correct the accumulated decoding error during sampling, and propose classifier-free guidance for conditional missing column value imputation. Comprehensive experiments on seven datasets demonstrate that TabDiff achieves superior average performance over existing competitive baselines across all eight metrics, with up to $22.5\%$ improvement over the state-of-the-art model on pair-wise column correlation estimations. Code is available at https://github.com/MinkaiXu/TabDiff.

翻译：合成高质量的表格数据是众多数据科学任务中的重要课题，涵盖从数据集增强到隐私保护等多个方面。然而，由于表格数据固有的异构数据类型、复杂的相互关联以及精细的列级分布，为其开发具有强表达能力的生成模型颇具挑战性。本文提出TabDiff，一种联合扩散框架，可在单一模型中建模表格数据的所有多模态分布。我们的核心创新在于为数值型和分类型数据设计了一个联合连续时间扩散过程，其中我们提出了特征级可学习的扩散过程，以应对不同特征分布的高度差异性。TabDiff通过一个处理不同输入类型的transformer进行参数化，整个框架能够以端到端的方式高效优化。我们进一步引入了一种多模态随机采样器，以自动校正采样过程中累积的解码误差，并提出了用于条件缺失列值填补的无分类器指导。在七个数据集上的综合实验表明，TabDiff在所有八项指标上均优于现有竞争基线，取得了卓越的平均性能，其中在成对列相关性估计任务上，相较于最先进模型提升了高达$22.5\%$。代码发布于https://github.com/MinkaiXu/TabDiff。