# Dependency-aware synthetic tabular data generation

arXiv: https://arxiv.org/abs/2507.19211 · HTML: https://mlsanity.com/paper/2507.19211/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 25 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [NVAE: A Deep Hierarchical Variational Autoencoder](https://mlsanity.com/paper/2007.03898/) (2020)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space](https://mlsanity.com/paper/2310.09656/) (2023)
- [PacGAN: The power of two samples in generative adversarial networks](https://mlsanity.com/paper/1712.04086/) (2017)
- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [TabuLa: Harnessing Language Models for Tabular Data Synthesis](https://mlsanity.com/paper/2310.12746/) (2023)
- [From GAN to WGAN](https://mlsanity.com/paper/1904.08994/) (2019)
- [Kamino: Constraint-Aware Differentially Private Data Synthesis](https://mlsanity.com/paper/2012.15713/) (2020)
- [CuTS: Customizable Tabular Synthetic Data Generation](https://mlsanity.com/paper/2307.03577/) (2023)
- [LLM-TabLogic: Preserving Inter-Column Logical Relationships in Synthetic Tabular Data via Prompt-Guided Latent Diffusion](https://mlsanity.com/paper/2503.02161/) (2025)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Preserving logical and functional dependencies in synthetic tabular data](https://mlsanity.com/paper/2409.17684/) (2024)
- [CausalDiffTab: Mixed-Type Causal-Aware Diffusion for Tabular Data Generation](https://mlsanity.com/paper/2506.14206/) (2025)
- [Convex space learning for tabular synthetic data generation](https://mlsanity.com/paper/2407.09789/) (2024)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework](https://mlsanity.com/paper/2605.28198/) (2026)
- [Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents](https://mlsanity.com/paper/2608.15109/) (2026)

## Abstract (arXiv)

Synthetic tabular data is increasingly used in privacy-sensitive domains such as health care, but existing generative models often fail to preserve inter-attribute relationships. In particular, functional dependencies (FDs) and logical dependencies (LDs), which capture deterministic and rule-based associations between features, are rarely or often poorly retained in synthetic datasets. To address this research gap, we propose the Hierarchical Feature Generation Framework (HFGF) for synthetic tabular data generation. We created benchmark datasets with known dependencies to evaluate our proposed HFGF. The framework first generates independent features using any standard generative model, and then reconstructs dependent features based on predefined FD and LD rules. Our experiments on four benchmark datasets with varying sizes, feature imbalance, and dependency complexity demonstrate that HFGF improves the preservation of FDs and LDs across six generative models, including CTGAN, TVAE, and GReaT. Our findings demonstrate that HFGF can significantly enhance the structural fidelity and downstream utility of synthetic tabular data.