TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

# TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling
Source: [https://arxiv.org/abs/2606.31268](https://arxiv.org/abs/2606.31268)
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> Abstract:The growing demand for privacy\-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows\. Despite notable advances in generative modeling, existing solutions often lack integration of adaptive generation strategies, multi\-metric evaluation, and accessible end\-to\-end generators within a unified web\-based toolkit\. In this work, we introduce TDGT \(Tabular Data Generation Toolkit\), a web\-based toolkit for synthetic tabular data generation and fidelity assessment\. TDGT introduces the Adaptive Bayesian Mixture Synthesizer \(ABMS\), a novel algorithm that autonomously determines the optimal number of mixture components through iterative cluster quality optimization, eliminating the need for manual hyperparameter configuration\. Building upon ABMS, we further propose VAE\-ABMS, a hybrid architecture that couples Variational Autoencoder\-based latent space learning with adaptive Bayesian mixture synthesis, enabling high\-fidelity generation of complex, nonlinear tabular distributions\. For large\-scale scenarios, TDGT provides a GPU\-accelerated variant of ABMS leveraging CUDA\-based k\-means clustering and Gaussian mixture fitting\. Synthetic data fidelity is assessed through eleven statistical fidelity metrics spanning distributional divergence, structural correlation, and sample\-level similarity, complemented by privacy risk indicators including k\-anonymity scoring and disclosure rate estimation\. The web\-based toolkit supports a real\-time streaming interface with interactive Plotly\-based visualizations\. TDGT is assessed across datasets from healthcare, socioeconomic modeling, and cybersecurity domains, demonstrating consistent generation fidelity and statistical coherence across heterogeneous feature types and data scales\.

## Submission history

From: Vasileios Pezoulas Dr\. \[[view email](https://arxiv.org/show-email/6828696e/2606.31268)\] **\[v1\]**Tue, 30 Jun 2026 07:42:48 UTC \(4,068 KB\)