TabDDPM: Modelling Tabular Data with Diffusion Models (2209.15421v2)

Abstract: Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the framework of diffusion models can be advantageous for general tabular problems, where datapoints are typically represented by vectors of heterogeneous features. The inherent heterogeneity of tabular data makes it quite challenging for accurate modeling, since the individual features can be of completely different nature, i.e., some of them can be continuous and some of them can be discrete. To address such data types, we introduce TabDDPM -- a diffusion model that can be universally applied to any tabular dataset and handles any type of feature. We extensively evaluate TabDDPM on a wide set of benchmarks and demonstrate its superiority over existing GAN/VAE alternatives, which is consistent with the advantage of diffusion models in other fields. Additionally, we show that TabDDPM is eligible for privacy-oriented setups, where the original datapoints cannot be publicly shared.

• The paper introduces TabDDPM as a novel diffusion model that combines multinomial and Gaussian processes to effectively manage heterogeneous tabular data.
• The methodology outperforms alternatives like GANs and VAEs in generating high-quality synthetic data while preserving privacy.
• Experimental results demonstrate that models trained on TabDDPM-generated data achieve competitive machine learning performance without compromising data privacy.

TabDDPM: Advancing Diffusion Models for Tabular Data

The utilization of denoising diffusion probabilistic models (DDPM) has significantly expanded across various fields of generative modeling. While DDPMs are predominantly recognized in computer vision, they have gradually been adapted to other domains such as NLP, speech, and graph data. In their paper, Kotelnikov et al. propose TabDDPM, a novel diffusion model specifically designed for modeling tabular data, characterized by its heterogeneous nature, combining both continuous and discrete features.

Contributions and Methodology

The main contributions presented in the paper can be summarized as follows:

1. Introduction of TabDDPM: The authors develop TabDDPM as a DDPM variant capable of handling the intrinsic complexity of tabular data. The model effectively processes a mix of numerical and categorical features by employing multinomial diffusion for categorical data and Gaussian diffusion for numerical data. The model's architecture involves a multi-layer neural network that manages these dual processing streams to accurately model tabular data.
2. Comparison with Alternative Approaches: Through empirical evaluations against prominent models like GANs and VAEs, TabDDPM demonstrates superior performance benchmarks. By assessing the generative quality, the results highlight diffusion models' edge in producing realistic synthetic data.
3. Implications for Privacy: The paper emphasizes TabDDPM's applicability in privacy-focused environments, where real data sharing is restricted due to regulations like GDPR. This aspect is particularly vital as synthetic data generated by TabDDPM can offer a viable alternative in these contexts without compromising user privacy.

Experimental Results

The authors conduct a comprehensive assessment across numerous real-world datasets to validate TabDDPM's effectiveness. The experiments show that TabDDPM achieves state-of-the-art results in terms of machine learning efficiency, where models trained on TabDDPM-generated data perform competitively against models trained on real data.

Moreover, the results underline TabDDPM's capability to maintain high utility while preserving privacy, a critical need in many industrial applications. The experiments also reveal that simpler interpolation methods like SMOTE, although competitive in some aspects, fail to match TabDDPM's performance when privacy is a concern.

Practical and Theoretical Implications

The introduction of TabDDPM signifies a meaningful step forward in the application of diffusion models beyond their traditional domains. The model addresses two critical challenges inherent in tabular data: feature heterogeneity and dataset size. By doing so, it opens new opportunities to leverage synthetic data for various applications, including those constrained by privacy issues.

Theoretically, this work enriches the understanding of diffusion models' versatility and provides a foundation for further exploration within tabular settings. Future research paths may involve refining the method's efficiency and scalability or extending its applicability to other forms of structured data.

Conclusion

TabDDPM emerges as a robust solution for modeling tabular data without sacrificing privacy or realism. While diffusion models continue to be refined, the contribution of TabDDPM offers an innovative perspective on addressing the challenges associated with tabular datasets. This work not only advances the potential of diffusion models but also showcases their adaptability and relevance across diverse data regimes.