# Preserving logical and functional dependencies in synthetic tabular data

arXiv: https://arxiv.org/abs/2409.17684 · HTML: https://mlsanity.com/paper/2409.17684/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 31 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [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)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [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)
- [Diffusion models for missing value imputation in tabular data](https://mlsanity.com/paper/2210.17128/) (2022)
- [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)
- [Learning Models over Relational Data using Sparse Tensors and Functional Dependencies](https://mlsanity.com/paper/1703.04780/) (2017)
- [Convex space learning for tabular synthetic data generation](https://mlsanity.com/paper/2407.09789/) (2024)

## Built on by (most cited first, 4 of 4)

- [Dependency-aware synthetic tabular data generation](https://mlsanity.com/paper/2507.19211/) (2025)
- [K-IPO: Kendall-constrained Importance Preserving Oversampling for Imbalanced Tabular Data](https://mlsanity.com/paper/2607.16478/) (2026)
- [Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents](https://mlsanity.com/paper/2608.15109/) (2026)
- [Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps](https://mlsanity.com/paper/2610.00391/) (2026)

## Abstract (arXiv)

Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthetic data is yet to be explored. In addition to the existing notion of functional dependencies, we introduce the notion of logical dependencies among the attributes in this article. Moreover, we provide a measure to quantify logical dependencies among attributes in tabular data. Utilizing this measure, we compare several state-of-the-art synthetic data generation algorithms and test their capability to preserve logical and functional dependencies on several publicly available datasets. We demonstrate that currently available synthetic tabular data generation algorithms do not fully preserve functional dependencies when they generate synthetic datasets. In addition, we also showed that some tabular synthetic data generation models can preserve inter-attribute logical dependencies. Our review and comparison of the state-of-the-art reveal research needs and opportunities to develop task-specific synthetic tabular data generation models.