# An improved tabular data generator with VAE-GMM integration

arXiv: https://arxiv.org/abs/2404.08434 · HTML: https://mlsanity.com/paper/2404.08434/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 32 references that are in the map.
- First posted on arXiv 12 Apr 2024.

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

- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [TabNet: Attentive Interpretable Tabular Learning](https://mlsanity.com/paper/1908.07442/) (2019)
- [Deep Neural Networks and Tabular Data: A Survey](https://mlsanity.com/paper/2110.01889/) (2021)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders](https://mlsanity.com/paper/1611.02648/) (2016)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [MuseGAN: Multi-track Sequential Generative Adversarial Networks for Symbolic Music Generation and Accompaniment](https://mlsanity.com/paper/1709.06298/) (2017)
- [Inference Suboptimality in Variational Autoencoders](https://mlsanity.com/paper/1801.03558/) (2018)
- [MidiNet: A Convolutional Generative Adversarial Network for Symbolic-domain Music Generation](https://mlsanity.com/paper/1703.10847/) (2017)
- [VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data](https://mlsanity.com/paper/2006.11941/) (2020)
- [Adversarial Feature Matching for Text Generation](https://mlsanity.com/paper/1706.03850/) (2017)
- [Adversarial Ranking for Language Generation](https://mlsanity.com/paper/1705.11001/) (2017)
- [Game Level Clustering and Generation using Gaussian Mixture VAEs](https://mlsanity.com/paper/2009.09811/) (2020)

## Built on by (most cited first, 13 of 13)

- [HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection](https://mlsanity.com/paper/2408.02927/) (2024)
- [Synthetic Tabular Data Validation: A Divergence-Based Approach](https://mlsanity.com/paper/2405.07822/) (2024)
- [Generative Active Adaptation for Drifting and Imbalanced Network Intrusion Detection](https://mlsanity.com/paper/2503.03022/) (2025)
- [TabularQGAN: A quantum generative model for tabular data synthesis](https://mlsanity.com/paper/2505.22533/) (2025)
- [Artificial Inductive Bias for Synthetic Tabular Data Generation in Data-Scarce Scenarios](https://mlsanity.com/paper/2407.03080/) (2024)
- [LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models](https://mlsanity.com/paper/2505.14752/) (2025)
- [A Closer Look on Memorization in Tabular Diffusion Model: A Data-Centric Perspective](https://mlsanity.com/paper/2505.22322/) (2025)
- [Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data](https://mlsanity.com/paper/2507.10998/) (2025)
- [Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning](https://mlsanity.com/paper/2605.04911/) (2026)
- [Probabilistic causal graphs as categorical data synthesizers: Do they do better than Gaussian Copulas and Conditional Tabular GANs?](https://mlsanity.com/paper/2504.11547/) (2025)
- [Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach](https://mlsanity.com/paper/2512.04223/) (2025)
- [Controllable Generative Sandbox for Causal Inference](https://mlsanity.com/paper/2603.03587/) (2026)
- [A Systematic Framework for Tabular Data Disentanglement](https://mlsanity.com/paper/2604.07940/) (2026)

## Abstract (arXiv)

The rising use of machine learning in various fields requires robust methods to create synthetic tabular data. Data should preserve key characteristics while addressing data scarcity challenges. Current approaches based on Generative Adversarial Networks, such as the state-of-the-art CTGAN model, struggle with the complex structures inherent in tabular data. These data often contain both continuous and discrete features with non-Gaussian distributions. Therefore, we propose a novel Variational Autoencoder (VAE)-based model that addresses these limitations. Inspired by the TVAE model, our approach incorporates a Bayesian Gaussian Mixture model (BGM) within the VAE architecture. This avoids the limitations imposed by assuming a strictly Gaussian latent space, allowing for a more accurate representation of the underlying data distribution during data generation. Furthermore, our model offers enhanced flexibility by allowing the use of various differentiable distributions for individual features, making it possible to handle both continuous and discrete data types. We thoroughly validate our model on three real-world datasets with mixed data types, including two medically relevant ones, based on their resemblance and utility. This evaluation demonstrates significant outperformance against CTGAN and TVAE, establishing its potential as a valuable tool for generating synthetic tabular data in various domains, particularly in healthcare.