Synthetic Tabular Data Generation

Ask about this topic

Overview

The field studies how to create artificial tables with realistic numerical and categorical values, dependencies, and—in some settings—relational or temporal structure. Approaches include GANs and VAEs, copulas and Bayesian networks, diffusion and flow models, tree-based generators, and language-model methods, often augmented with causal knowledge or hard domain constraints. Synthetic tables can support data sharing, model training, rare-event augmentation, and analysis when real records are scarce or sensitive; evaluation therefore considers statistical fidelity, downstream utility, privacy, and fairness. A central tension is that matching marginals or predictive performance may still miss dependencies, rare patterns, causal effects, and valid domain rules, while complex deep generators do not consistently outperform simpler statistical baselines, especially on small datasets.

Latest papers

CardsList

99 papers14 papers in the last four weeks, up 250% on the four weeks before. 0.1% of all new papers.

  1. Faithful Federated Tabular Data Synthesis

    Oct 24, 2026Zilong Cao, Hai Zhang, Xuan BiSynthetic Data GenerationSynthetic Tabular Data Generation

    School of Mathematics, Northwest University · Carlson School of Management, University of Minnesota

  2. DiffLTM: Continuous Diffusions Large Tabular Models for Conditional Generation

    Oct 21, 2026Alankrita Bhatt, Mukur Gupta, Andrea Montanari +1Tabular Diffusion ModelsSynthetic Tabular Data Generation

    Granica Computing Inc. · Stanford University

  3. Classical methods for modern applications: synthetic tabular data generation using lightweight discrete response continuous latent variable model

    Oct 15, 2026Bartosz Barzdajn, Mateusz KrzysztońSynthetic Tabular Data GenerationLatent Variable Models

    NASK National Research Institute

  4. TabGift: A Context-Efficient Foundation Model for Tabular Data Generation

    Oct 12, 2026Yu-Sheng Li, Jun-Peng Jiang, Han-Jia YeSynthetic Tabular Data GenerationGenerative Modeling

    School of Artificial Intelligence, Nanjing University, China · National Key Laboratory for Novel Software Technology, Nanjing University, China

  5. TabMosaic: Unified In-Context Diffusion for Tabular Generative Tasks

    Oct 11, 2026Jayoung Kim, Sanghyun Hong, Noseong ParkSynthetic Tabular Data GenerationTabular Diffusion Models

    KAIST · Oregon State University

  6. Beyond Distributional Fidelity: Causal-Penalized Diffusion for Synthetic Tabular Data

    Oct 8, 2026Lan Tao, Yongxian He, Shirong Xu +2Synthetic Tabular Data GenerationTabular Diffusion Models

    Department of Statistics & Data Science, University of California, Los Angeles · Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego · Wang Yanan Institute for Studies in Economics, Xiamen University

  7. Below what training size do deep tabular generators stop beating trivial baselines? A preregistered benchmark on a size ladder of clinical and standard datasets

    Oct 2, 2026Shivam ShrivastavaSynthetic Tabular Data GenerationSynthetic Data Evaluation

    VIT Bhopal University, India

  8. PEG-Tab: Sampling-Time Record Repair and Release Control for Tabular Synthesis

    Sep 30, 2026Pengfei Li, QinYi Liu, Mohammad KhalilSynthetic Data GenerationSynthetic Tabular Data Generation

  9. MIND: Marginal-Invariant Neural Dependency Diffusion for Mixed-Type Tabular Generation

    Sep 30, 2026Pengfei Li, Mohammad KhalilCopula ModelsSynthetic Tabular Data Generation

    Centre for the Science of Learning & Technology (SLATE), University of Bergen, Bergen, Norway

  10. Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps

    Sep 30, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data GenerationSynthetic Tabular Data Generation

    Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

  11. CDMD: A Cross-Dataset Mixed-Type Diffusion Model for Tabular Data

    Sep 30, 2026Mohamed Amine Ketata, Maximilian Schambach, Stephan GünnemannMasked Diffusion ModelsTabular Diffusion Models

    Munich Data Science Institute, Technical University of Munich, Germany · SAP SE, Germany

  12. Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

    Sep 29, 2026Zilong Zhao, Abdul Raheem, Jiayu Li +5Constrained Generative ModelingSynthetic Data Generation

    Betterdata AI · National University of Singapore · University of Illinois Urbana-Champaign +1

  13. From Data to Program: Fast & Direct Generative Program Inference from Empirical Data

    Sep 28, 2026Simon Klüttermann, Xueying Ding, Leman AkogluProgram SynthesisSynthetic Tabular Data Generation

    Carnegie Mellon University

  14. Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation

    Sep 28, 2026Michael Vasilakakis, Dimitris K. IakovidisSynthetic Data GenerationSynthetic Tabular Data Generation

    Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece

  15. Synthesis Through Simulation: Generating Coherent Enterprise Data via Scalable Agent-System Interaction

    Sep 24, 2026Yipeng Li, Ashutosh Hathidara, Jane Lo +3Synthetic Data GenerationSynthetic Data Generation for LLM Agents

    SAP Labs

  16. Beyond Distribution Matching: Semantics-Consistent Tabular Diffusion with Weak Semantic Priors

    Sep 13, 2026Yili Wang, Ruxue Shi, Mengnan Du +3Synthetic Tabular Data GenerationTabular Diffusion Models

    School of Artificial Intelligence, Jilin University, Changchun, China · School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen, China

  17. LEBGen: An LLM-Enhanced Bayesian Network Framework for Few-Shot Travel Survey Data Generation

    Sep 8, 2026Zijian Shen, Bin Zhou, Jiguang Wang +2Bayesian NetworksSynthetic Data Generation

    Department of Civil Engineering, The University of Hong Kong · Department of Urban Planning and Design, The University of Hong Kong

  18. Portable Causal Fairness Across Synthetic Data Generator Families

    Sep 2, 2026Steven Golob, Sikha Pentyala, Martine De CockAlgorithmic FairnessSynthetic Tabular Data Generation

    University of Washington Tacoma

  19. Creation begins with understanding: LLMs as strategy designers for privacy-preserving tabular data synthesis

    Aug 30, 2026Jinmeng Li, Quan Zhang, Hangting Ye +4Synthetic Tabular Data GenerationPrivacy-Preserving ML

    School of Artificial Intelligence, Jilin University, Changchun, China · Broad College of Business, Michigan State University, East Lansing, USA · Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia +2

  20. Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes

    Aug 16, 2026Farbod Abbasi, Zachary Patterson, Bilal FarooqSynthetic Population GenerationSynthetic Data Generation

    Concordia University, Montreal, Quebec, Canada · Toronto Metropolitan University, Toronto, Ontario, Canada

  21. Constraint-Aware Synthetic Tabular Data Generation via Inter-Column Constraint Discovery with LLM Agents

    Aug 15, 2026Jianxing Zhao, Mao Guan, Dongyu LiuSynthetic Tabular Data Generation

    University of California, Davis · independent researcher

  22. Generating Benchmark Health Data Using a Tabular Diffusion Transformer

    Aug 14, 2026Hao Yan, Lisa Pilgram, Dan Liu +3Synthetic Tabular Data GenerationTabular Diffusion Models

    Children’s Hospital of Eastern Ontario Research Institute, Ontario, Canada · School of Epidemiology and Public Health, University of Ottawa, Ontario, Canada · Department of Nephrology and Medical Intensive Care, Charité - Universitaetsmedizin Berlin, Berlin, Germany +2

  23. CoMedBench: A Multi-Source Benchmark of Synthetic Medical Data Fidelity and Downstream Utility

    Aug 13, 2026Akanta Das, Farhad Al-Amin Dipto, Mrinmoy Sarkar Anto +3Benchmark DesignSynthetic Data Generation

    Bangladesh University of Engineering and Technology, Dhaka, Bangladesh · School of Medicine, Stanford University, Stanford, California, United States

  24. TailBooster: A Dual-Layer Generative Framework for Extreme Value Augmentation with Operational Validity Enforcement

    Aug 12, 2026Karim Aly, Alexei Sharpanskykh, Jacco HoekstraData AugmentationSynthetic Tabular Data Generation

    Control and Operations, Faculty of Aerospace Engineering, Delft University of Technology (TU Delft), Kluyverweg 1, Delft, 2629 HS, South Holland, Netherlands

  25. FUSE: Feature-Wise Unified Specialization with Cross-Column Exchange for Mixed-Type Tabular Flow Matching

    Aug 7, 2026Suman Cha, Seongchan Lee, Dohyun Ko +1Flow MatchingSynthetic Tabular Data Generation

    Department of Statistics and Data Science, Yonsei University · Department of Mathematical Sciences, KAIST

  26. LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation

    Aug 3, 2026Zijian Shen, Taijie Chen, Bin Zhou +2Bayesian Network Structure LearningBayesian Networks

    Department of Civil Engineering, The University of Hong Kong

  27. FairDiffuseVQVAE: Sampling-Time Fairness in Tabular Diffusion via Conditional Refinement of Vector-Quantized Latents

    Jul 31, 2026Nitish Nagesh, Mahdi Bagheri, Amir M. RahmaniAlgorithmic FairnessDiffusion Sampling

    University of California, Irvine

  28. Logit-Coordinate Generative Models for Mixed Continuous-Categorical Tabular Data

    Jul 25, 2026Yuefei Shen, Xiaotong ShenFlow MatchingSynthetic Tabular Data Generation

    School of Statistics, University of Minnesota

  29. SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework

    Jul 21, 2026Akarsh K Nair, Muhammad Arifur Rahman, Nicholas Shopland +8Synthetic Data PretrainingClinical Outcome Prediction

    Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom · Division of Epidemiology and Public Health, University of Nottingham, Nottingham, United Kingdom · Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia +1

  30. Measuring the Dependency Gap: Diagnosing Inter-Column Fidelity in Tabular Generative Models

    Jul 20, 2026Jie ZhangTwo-Sample TestingSynthetic Tabular Data Generation

    Accenture, Tokyo 107-8672, Japan