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[2602.21948] Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis
Summary
This paper introduces GACTGAN, a Bayesian Generative Adversarial Network that utilizes Gaussian approximation for synthesizing tabular data, improving efficiency and data integrity compared to traditional CTGAN methods.
Why It Matters
The development of GACTGAN addresses the limitations of existing models in synthesizing tabular data while managing the risk-utility trade-off. This is crucial for applications requiring high-quality synthetic data with privacy considerations, making it relevant for data scientists and machine learning practitioners.
Key Takeaways
- GACTGAN integrates Bayesian techniques to enhance tabular data synthesis.
- It reduces computational overhead compared to traditional MCMC methods.
- The model preserves tabular structure and inferential statistics more effectively.
- GACTGAN presents a simpler implementation for generating synthetic data.
- This approach mitigates privacy risks associated with data synthesis.
Computer Science > Machine Learning arXiv:2602.21948 (cs) [Submitted on 25 Feb 2026] Title:Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis Authors:Bahrul Ilmi Nasution, Mark Elliot, Richard Allmendinger View a PDF of the paper titled Bayesian Generative Adversarial Networks via Gaussian Approximation for Tabular Data Synthesis, by Bahrul Ilmi Nasution and 2 other authors View PDF HTML (experimental) Abstract:Generative Adversarial Networks (GAN) have been used in many studies to synthesise mixed tabular data. Conditional tabular GAN (CTGAN) have been the most popular variant but struggle to effectively navigate the risk-utility trade-off. Bayesian GAN have received less attention for tabular data, but have been explored with unstructured data such as images and text. The most used technique employed in Bayesian GAN is Markov Chain Monte Carlo (MCMC), but it is computationally intensive, particularly in terms of weight storage. In this paper, we introduce Gaussian Approximation of CTGAN (GACTGAN), an integration of the Bayesian posterior approximation technique using Stochastic Weight Averaging-Gaussian (SWAG) within the CTGAN generator to synthesise tabular data, reducing computational overhead after the training phase. We demonstrate that GACTGAN yields better synthetic data compared to CTGAN, achieving better preservation of tabular structure and inferential statistics with less privacy risk. These results highlight GACTGAN ...
