Synthetic Tabular Data Generation

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8 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation
Jun 8, 2026Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali +2Copula Models Synthetic Tabular Data Generation
1,2,3,5Lane Department of Computer Science and Electrical Engineering West Virginia University, Morgantown, WV 26506, USA · 4Scientific Computing and Imaging Institute & Department of Biomedical Informatics The University of Utah, Salt Lake City, UT 84112, USA
Synthetic but Not Realistic: The Evaluation Challenge in Generative Modelling for Structured Electronic Medical Records
Jun 8, 2026Nicholas I-Hsien Kuo, Blanca Gallego, Louisa JormSynthetic Tabular Data Generation Electronic Health Records
Centre for Big Data Research in Health, the University of New South Wales, Sydney, Australia
Declarative Outcome-Conformant Synthesis: Exact, Closed-Form Specification Satisfaction and a Conformance Benchmark
Jun 7, 2026Muhammed RasinSynthetic Tabular Data Generation Synthetic Benchmark Generation
Independent Researcher
SoK: Reconstruction Attacks on Synthetic Tabular Data (Insights from Winning the NIST CRC)
Jun 6, 2026Steven Golob, Sikha Pentyala, Martine De CockPrivacy Auditing Adversarial Attacks
School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA, USA · Department of Mathematics, Computer Science, and Statistics, Ghent University, Gent, Belgium
Differentially Private Synthetic Data via APIs 4: Tabular Data
Jun 6, 2026Toan Tran, Arturs Backurs, Zinan Lin +3Synthetic Data Generation Synthetic Tabular Data Generation
Emory University · Microsoft Research
AugMask: Training Diffusion Models on Incomplete Tabular Data via Stochastic Augmentation and Masking
Jun 2, 2026Jungkyu Kim, Taeyoung Park, Kibok LeeLearning with Missing Data Tabular Diffusion Models
Department of Statistics and Data Science, Yonsei University, South Korea.
It does what it says on the tin: safe synthetic data from coarsened margins
Jun 1, 2026Gillian M RaabSynthetic Tabular Data Generation
University of Edinburgh and the Scottish Centre for Administrative Data Research
Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
May 27, 2026Junfeng Nie, Alvin Jin, Xiaohui ChenSynthetic Data Generation Synthetic Tabular Data Generation
AnyFluxion · University of Southern California
Generating Logically Consistent Synthetic Supply Chain Data with LLM-Driven Knowledge Graph Reasoning
May 26, 2026Yunbo Long, Ge Zheng, Liming Xu +1KG Construction Synthetic Data Generation
Department of Engineering, University of Cambridge, Cambridge, United Kingdom · The Alan Turing Institute, London, United Kingdom
PATE-TabTransGAN: Differentially Private Synthetic Tabular Data Generation via Transformer-Based Student Discrimination
May 26, 2026M. Youssef, M. WoźniakSynthetic Tabular Data Generation Differential Privacy
Wrocław University of Science and Technology, Wrocław, Poland
Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets
May 18, 2026Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti +8Healthcare Synthetic Tabular Data Generation
University of California, Irvine, CA, USA · PathAI, USA
TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
May 17, 2026Meysam Alishahi, Yan Zheng, Junpeng Wang +2Copula Models Synthetic Tabular Data Generation
University of Utah · Visa Research
ReMIA: a Powerful and Efficient Alternative to Membership Inference Attacks against Synthetic Data Generators
May 14, 2026Davide Scassola, Andrea Coser, Sebastiano SaccaniMembership Inference Attacks Synthetic Tabular Data Generation
Aindo SpA Trieste, Italy
Active Tabular Augmentation via Policy-Guided Diffusion Inpainting
May 11, 2026Zheyu Zhang, Shuo Yang, Bardh Prenkaj +1Data Augmentation Tabular Diffusion Models
Technical University of Munich · Munich Center for Machine Learning (MCML)
Concordia: Self-Improving Synthetic Tables for Federated LLMs
May 11, 2026Jimin Huang, Duanyu Feng, Nuo Chen +8LLM Fine-Tuning Synthetic Data Generation
University of Manchester · The Fin AI · National University of Singapore +5
Tabular Foundation Model for Generative Modelling
May 10, 2026Xiangjian Jiang, Mingxuan Liu, Nikola Simidjievski +2Tabular Foundation Models Synthetic Tabular Data Generation
Department of Computer Science and Technology, University of Cambridge, UK · SAP SE · Télécom Paris, Institut Polytechnique de Paris, France +1
Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention
May 5, 2026Daniel Mensing, Jan Kapar, Jochen G. Hirsch +3Synthetic Tabular Data Generation Multimodal Generation
Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany · Leibniz Institute for Prevention Research and Epidemiology – BIPS, Bremen, Germany · Faculty of Mathematics and Computer Science, University of Bremen, Bremen, Germany +1
Accurate and Robust Generative Approach for Overcoming Data Sparsity and Imbalance in Landslide Modeling with A Tabular Foundation Model
Apr 28, 2026Kaixuan Shao, Gang Mei, Yinghan Wu +2Class-Imbalanced Learning Synthetic Tabular Data Generation
School of Engineering and Technology, China University of Geosciences (Beijing), 100083, Beijing, China · School of Geological Engineering and Geomatics, Chang’an University, 710064, Xi’an, China
SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation
Apr 27, 2026Shuo Yang, Zheyu Zhang, Bardh Prenkaj +1Synthetic Tabular Data Generation
Technical University of Munich
Generative Synthetic Data for Causal Inference: Pitfalls, Remedies, and Opportunities
Apr 26, 2026Yichen XuCausal Effect Estimation Synthetic Data Generation
University of California, Berkeley
TabSCM: A practical Framework for Generating Realistic Tabular Data
Apr 24, 2026Sven Jacob, Bardh Prenkaj, Weijia Shao +1Structural Causal Models Causal Counterfactual Generation
Federal Institute for Occupational Safety and Health (BAuA), Dresden, Germany · School of Computation, Information and Technology, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning (MCML)
Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models
Apr 22, 2026Tapiwa Amion Chinodakufa, Ashfaq Ali Shafin, Khandaker Mamun AhmedSynthetic Data Generation Educational Technology
The Beacom College of Computer & Cyber Sciences, Dakota State University · Knight Foundation School of Computing & Information Sciences, Florida International University
Synthetic Flight Data Generation Using Generative Models
Apr 22, 2026Karim Aly, Alexei SharpanskykhSynthetic Data Generation Synthetic Tabular Data Generation
Faculty of Aerospace Engineering · Delft University of Technology (TU Delft) · Delft, The Netherlands
Self-Improving Tabular Language Models via Iterative Reward-Guided Post-Training
Apr 21, 2026Yunbo Long, Tejumade Afonja, Guangya Hao +2Tabular Foundation Models Group Relative Policy Optimization
Department of Engineering, University of Cambridge · CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · The Alan Turing Institute, London
Self-Reinforcing Controllable Synthesis of Rare Relational Data via Bayesian Calibration
Apr 18, 2026Chongsheng Zhang, Hao Wang, Zelong Yu +7Class-Imbalanced Learning Synthetic Data Augmentation
Henan University, China · Department of Statistics, LMU Munich · MCML Munich +1
Evaluating quality in synthetic data generation for large tabular health datasets
Apr 17, 2026Jean-Baptiste Escudié, Benjamin Barnes, Stefan Meisegeier +3Healthcare Synthetic Tabular Data Generation
Centre for Artificial Intelligence in Public Health Research, Robert Koch Institute · German Centre for Cancer Registry Data, Robert Koch Institute · Medical Informatics, Berlin Institute of Health at Charité - Universitätsmedizin Berlin
Decoupling Identity from Utility: Privacy-by-Design Frameworks for Financial Ecosystems
Apr 16, 2026Ifayoyinsola Ibikunle, Tyler Farnan, Senthil Kumar +1Synthetic Data Generation Synthetic Tabular Data Generation
Capital One
Synthesizing real-world distributions from high-dimensional Gaussian Noise with Fully Connected Neural Network
Apr 10, 2026Joanna KomorniczakSynthetic Tabular Data Generation Generative Modeling
Department of Systems and Computer Networks, Wrocław University of Science and Technology
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
Mar 10, 2026Davide Tugnoli, Andrea De Lorenzo, Marco Virgolin +1Tabular Foundation Models Synthetic Data Generation
Department of Mathematics, Informatics and Geosciences, University of Trieste, Trieste, Italy · InSilicoTrials Technologies, Riva Grumula 2, 34123 Trieste, Italy · Department of Engineering and Architecture, University of Trieste, Trieste, Italy +3
TabICLv2: A better, faster, scalable, and open tabular foundation model
Feb 11, 2026Jingang Qu, David Holzmüller, Gaël Varoquaux +1Synthetic Data Pretraining Tabular Foundation Models
SODA Team, INRIA Saclay, Palaiseau, France · Probabl, France

Jun 8, 2026, cs.LGJ/K move · Enter open · S save

BSTabDiff: Block-Subunit Diffusion Priors for High-Dimensional Tabular Data Generation

Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali, Gianfranco Doretto+1

1,2,3,5Lane Department of Computer Science and Electrical Engineering West Virginia University, Morgantown, WV 26506, USA · 4Scientific Computing and Imaging Institute & Department of Biomedical Informatics The University of Utah, Salt Lake City, UT 84112, USA

Copula Models Synthetic Tabular Data Generation Synthetic Benchmark Generation Generative Modeling

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High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.g., omics) are characterized by

n \ll m

, where

n

= number of samples, and

m

= number of features. Such domains often exhibit strong local correlation groups, sparse cross-group dependencies, heavy-tailed non-Gaussian marginals, heteroscedastic noise, and structured missingness, making direct density learning in

\mathbb{R}^m

ill-conditioned since

n \ll m

. We propose BSTabDiff, a block-subunit generative framework that partitions the

m

observed features into

M

latent blocks (

M \ll m

) and generates each block via a shared low-dimensional subunit variable, concentrating global dependence learning in the compact block-latent space

\mathbb{R}^M

while decoding to the full feature space with copula-driven dependence, flexible per-feature marginals, and explicit missingness mechanisms. BSTabDiff supports modern deep priors on block latents, including diffusion and normalizing flows, enabling stable synthesis and controllable benchmark generation in the HDLSS regime. Empirically, BSTabDiff produces more realistic and stable high-dimensional synthetic data when compared with unstructured tabular generators on HDLSS data.

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