# TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data

arXiv: https://arxiv.org/abs/2501.12012 · HTML: https://mlsanity.com/paper/2501.12012/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 59 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 30 of 33)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024)
- [Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation](https://mlsanity.com/paper/2406.06525/) (2024)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [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)
- [TabLLM: Few-shot Classification of Tabular Data with Large Language Models](https://mlsanity.com/paper/2210.10723/) (2022)
- [How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models](https://mlsanity.com/paper/2102.08921/) (2021)
- [Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space](https://mlsanity.com/paper/2310.09656/) (2023)
- [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)
- [TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation](https://mlsanity.com/paper/2111.08095/) (2021)
- [Neural Autoregressive Distribution Estimation](https://mlsanity.com/paper/1605.02226/) (2016)
- [REaLTabFormer: Generating Realistic Relational and Tabular Data using Transformers](https://mlsanity.com/paper/2302.02041/) (2023)
- [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)
- [Synthcity: facilitating innovative use cases of synthetic data in different data modalities](https://mlsanity.com/paper/2301.07573/) (2023)
- [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://mlsanity.com/paper/2204.00401/) (2022)
- [Training and Inference on Any-Order Autoregressive Models the Right Way](https://mlsanity.com/paper/2205.13554/) (2022)
- [Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees](https://mlsanity.com/paper/2309.09968/) (2023)
- [Generating Synthetic Mixed-type Longitudinal Electronic Health Records for Artificial Intelligent Applications](https://mlsanity.com/paper/2112.12047/) (2021)
- [TabMT: Generating tabular data with masked transformers](https://mlsanity.com/paper/2312.06089/) (2023)
- [Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data](https://mlsanity.com/paper/2304.03722/) (2023)
- [Row Conditional-TGAN for generating synthetic relational databases](https://mlsanity.com/paper/2211.07588/) (2022)
- [DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation](https://mlsanity.com/paper/2307.10430/) (2023)
- [Understanding and Mitigating Memorization in Diffusion Models for Tabular Data](https://mlsanity.com/paper/2412.11044/) (2024)
- [Strong statistical parity through fair synthetic data](https://mlsanity.com/paper/2311.03000/) (2023)

## Built on by (most cited first, 14 of 14)

- [RelDiff: Relational Data Generative Modeling with Graph-Based Diffusion Models](https://mlsanity.com/paper/2506.00710/) (2025)
- [Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Features](https://mlsanity.com/paper/2601.22816/) (2026)
- [Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure](https://mlsanity.com/paper/2603.10254/) (2026)
- [TabFORGE: A Tabular Foundation Model for Generative Modelling](https://mlsanity.com/paper/2605.09424/) (2026)
- [TABFAIRGDT: A Fast Fair Tabular Data Generator using Autoregressive Decision Trees](https://mlsanity.com/paper/2509.19927/) (2025)
- [Improving Predictions on Highly Unbalanced Data Using Open Source Synthetic Data Upsampling](https://mlsanity.com/paper/2507.16419/) (2025)
- [Democratizing Tabular Data Access with an Openunicodex2013Source Syntheticunicodex2013Data SDK](https://mlsanity.com/paper/2508.00718/) (2025)
- [A Note on Statistically Accurate Tabular Data Generation Using Large Language Models](https://mlsanity.com/paper/2505.02659/) (2025)
- [Disjoint Generation of Synthetic Data](https://mlsanity.com/paper/2507.19700/) (2025)
- [GEM-T: Generative Tabular Data via Fitting Moments](https://mlsanity.com/paper/2509.17752/) (2025)
- ["AGI" team at SHROOM-CAP: Data-Centric Approach to Multilingual Hallucination Detection using XLM-RoBERTa](https://mlsanity.com/paper/2511.18301/) (2025)
- [Autoregressive Synthesis of Sparse and Semi-Structured Mixed-Type Data](https://mlsanity.com/paper/2603.01444/) (2026)
- [Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data](https://mlsanity.com/paper/2607.15606/) (2026)
- [Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data](https://mlsanity.com/paper/2609.38414/) (2026)

## Abstract (arXiv)

Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications. We introduce the Tabular Auto-Regressive Generative Network (TabularARGN), a flexible framework designed to handle mixed-type, multivariate, and sequential datasets. By training on all possible conditional probabilities, TabularARGN supports advanced features such as fairness-aware generation, imputation, and conditional generation on any subset of columns. The framework achieves state-of-the-art synthetic data quality while significantly reducing training and inference times, making it ideal for large-scale datasets with diverse structures. Evaluated across established benchmarks, including realistic datasets with complex relationships, TabularARGN demonstrates its capability to synthesize high-quality data efficiently. By unifying flexibility and performance, this framework paves the way for practical synthetic data generation across industries.