# AutoDiff: combining Auto-encoder and Diffusion model for tabular data synthesizing

arXiv: https://arxiv.org/abs/2310.15479 · HTML: https://mlsanity.com/paper/2310.15479/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in Flow matching, time series and science, one of 14 directions in Diffusion models.
- Builds on 19 of its 30 references that are in the map.
- First posted on arXiv 24 Oct 2023.

## Placed in

- Diffusion models / [Flow matching, time series and science](https://mlsanity.com/diffusion/flow-matching-time-series-and-science/)

## Builds on (most cited first, 19 of 19)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models](https://mlsanity.com/paper/1810.01367/) (2018)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [Differentially Private Diffusion Models](https://mlsanity.com/paper/2210.09929/) (2022)
- [STaSy: Score-based Tabular data Synthesis](https://mlsanity.com/paper/2210.04018/) (2022)
- [CoDi: Co-evolving Contrastive Diffusion Models for Mixed-type Tabular Synthesis](https://mlsanity.com/paper/2304.12654/) (2023)
- [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://mlsanity.com/paper/2204.00401/) (2022)
- [DP-LDMs: Differentially Private Latent Diffusion Models](https://mlsanity.com/paper/2305.15759/) (2023)

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models](https://mlsanity.com/paper/2401.07187/) (2024)
- [A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond](https://mlsanity.com/paper/2503.05954/) (2025)
- [Automated data processing and feature engineering for deep learning and big data applications: a survey](https://mlsanity.com/paper/2403.11395/) (2024)
- [CTSyn: A Foundation Model for Cross Tabular Data Generation](https://mlsanity.com/paper/2406.04619/) (2024)
- [Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models](https://mlsanity.com/paper/2406.13012/) (2024)
- [Vision-Enhanced Time Series Forecasting via Latent Diffusion Models](https://mlsanity.com/paper/2502.14887/) (2025)
- [Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilities](https://mlsanity.com/paper/2405.20959/) (2024)
- [Diffusion and Flow Matching Models for Tabular Data: A Survey](https://mlsanity.com/paper/2502.17119/) (2025)
- [Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data](https://mlsanity.com/paper/2406.13130/) (2024)
- [Tabular Data Synthesis with Differential Privacy: A Survey](https://mlsanity.com/paper/2411.03351/) (2024)
- [TimeAutoDiff: A Unified Framework for Generation, Imputation, Forecasting, and Time-Varying Metadata Conditioning of Heterogeneous Time Series Tabular Data](https://mlsanity.com/paper/2406.16028/) (2024)
- [Privacy Auditing Synthetic Data Release through Local Likelihood Attacks](https://mlsanity.com/paper/2508.21146/) (2025)
- [Ensembling Membership Inference Attacks Against Tabular Generative Models](https://mlsanity.com/paper/2509.05350/) (2025)
- [Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis](https://mlsanity.com/paper/2509.18014/) (2025)
- [When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generation](https://mlsanity.com/paper/2512.08875/) (2025)
- [Recurrent Neural Goodness-of-Fit Test for Time Series](https://mlsanity.com/paper/2410.13986/) (2024)
- [Finding Connections: Membership Inference Attacks for the Multi-Table Synthetic Data Setting](https://mlsanity.com/paper/2602.07126/) (2026)
- [A Deep Generative Framework for Joint Households and Individuals Population Synthesis](https://mlsanity.com/paper/2407.01643/) (2024)
- [ReTabSyn: Realistic Tabular Data Synthesis via Reinforcement Learning](https://mlsanity.com/paper/2603.10823/) (2026)
- [SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation](https://mlsanity.com/paper/2604.24368/) (2026)
- [Boosting Predictive Performance on Tabular Data through Data Augmentation with Latent-Space Flow-Based Diffusion](https://mlsanity.com/paper/2511.16571/) (2025)
- [Privacy Preserving Diffusion Models for Mixed-Type Tabular Data Generation](https://mlsanity.com/paper/2512.00638/) (2025)
- [Stable and Privacy-Preserving Synthetic Educational Data with Empirical Marginals: A Copula-Based Approach](https://mlsanity.com/paper/2604.04195/) (2026)

## Abstract (arXiv)

Diffusion model has become a main paradigm for synthetic data generation in many subfields of modern machine learning, including computer vision, language model, or speech synthesis. In this paper, we leverage the power of diffusion model for generating synthetic tabular data. The heterogeneous features in tabular data have been main obstacles in tabular data synthesis, and we tackle this problem by employing the auto-encoder architecture. When compared with the state-of-the-art tabular synthesizers, the resulting synthetic tables from our model show nice statistical fidelities to the real data, and perform well in downstream tasks for machine learning utilities. We conducted the experiments over 15 publicly available datasets. Notably, our model adeptly captures the correlations among features, which has been a long-standing challenge in tabular data synthesis. Our code is available at https://github.com/UCLA-Trustworthy-AI-Lab/AutoDiffusion.