Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Zeyu Yang Peikun Guo Khadija Zanna Akane Sano
Diffusion models have emerged as a robust framework for various generative
tasks, such as image and audio synthesis, and have also demonstrated a
remarkable ability to generate mixed-type tabular data comprising both
continuous and discrete variables. However, current approaches to training
diffusion models on mixed-type tabular data tend to inherit the imbalanced
distributions of features present in the training dataset, which can result in
biased sampling. In this research, we introduce a fair diffusion model designed
to generate balanced data on sensitive attributes. We present empirical
evidence demonstrating that our method effectively mitigates the class
imbalance in training data while maintaining the quality of the generated
samples. Furthermore, we provide evidence that our approach outperforms
existing methods for synthesizing tabular data in terms of performance and
fairness.