Tabular Data Synthesis

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Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure
Sep 7, 2026Alexander DoudkinUser Simulation Tabular Data Synthesis
Minds AI Labs, Inc.
Differentially Private Paired Table-Image Multimodal Synthesis
Sep 1, 2026Kai Chen, Josephine Lamp, Somesh Jha +1Synthetic Data Generation Cross-Modal Alignment
University of Virginia · Dexcom · University of Wisconsin-Madison
PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization
Jun 16, 2026Arshia Ilaty, Hossein Shirazi, Manasi Chitale +5Synthetic Tabular Data Generation Tabular Data Synthesis
San Diego State University, San Diego, CA 92182 USA. · University of California, Irvine, CA 92697 USA.
LLM-as-a-Discriminator: When Synthetic Tables Still Look Real
Jun 1, 2026Manel Slokom, Malek Slokom, Thierno KantePrivacy Auditing Tabular Data Synthesis
Vrije Universiteit, Amsterdam · Equativ, Paris · EDICIA, Nantes
TabChange: Precise Attribute Changes in Tabular Data
May 30, 2026Arjun Dahal, Yu Lei, Raghu N. Kacker +1Adversarial Representation Learning Counterfactual Explanations
The University of Texas at Arlington · Arlington, Texas, USA · Information Technology Laboratory +2
Geometry-Aware Tabular Diffusion
May 23, 2026David Turtora ZagardoTabular Diffusion Models Tabular Data Synthesis
Independent Researcher.
FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models
May 12, 2026Abtin Mahyar, Masoumeh Shafieinejad, Yuhan Liu +1Membership Inference Attacks Tabular Diffusion Models
University of Waterloo · Vector Institute
On Privacy Leakage in Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics
May 7, 2026Masoumeh Shafieinejad, D. B. Emerson, Behnoosh Zamanlooy +5Data Leakage Membership Inference Attacks
Vector Institute · McMaster University · University of Toronto +1
Inference-Time Refinement Closes the Synthetic-Real Gap in Tabular Diffusion
May 7, 2026Eugenio Lomurno, Filippo Balzarini, Francesco Benelle +2Diffusion Model Guidance Inference-Time Optimization
Politecnico di Milano, AIRLab, Italy
Breaking the Quality-Privacy Tradeoff in Tabular Data Generation via In-Context Learning
May 6, 2026Xinyan Han, Yan Lu, Xiaoyu Lin +5Synthetic Data Augmentation Privacy-Preserving ML
1Tsinghua University · 2Southeast University · 3Siemens China
Causality for Tabular Data Synthesis: A High-Order Structure Causal Benchmark Framework
Jun 12, 2024Zineb Senane, Axel Karlsson, Lele Cao +6Synthetic Benchmark Generation Causal Inference
KTH Royal Institute of Technology · Stockholm, Sweden · King AI Labs, Microsoft +2

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Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure

Alexander Doudkin

Minds AI Labs, Inc.

User Simulation Tabular Data Synthesis Synthetic Data Evaluation

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Demographic synthetic survey panels are often validated by matching aggregate answers to published surveys. We test what that certificate establishes across six multiselect batteries from four survey organisations in three countries. The headline analysis is restricted to three instruments whose synthetic cohort and human target share the stated population frame; three other batteries remain sensitivity analyses. The response contract dominates measured fidelity. In the aligned instruments, committed sets leave 66 of 128 model-battery option slots empty in panels of up to 500 respondents, versus 0 of 128 under per-option probability elicitation. Across eight uncapped model-instrument comparisons, probabilities reduce option-marginal MAE by 4.53 to 7.30 points. The capped instrument reverses on two models until the vectors are projected onto its stated maximum. These are measurement effects: human targets are realised check-all responses, whereas the vectors are latent inclusion propensities. Published marginal agreement also fails to discriminate respondent simulation from direct population estimation. On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons. Constraint-aware probability vectors average 5.34 and beat the query on four of nine, so the baseline challenges the validation criterion rather than proving direct estimation uniformly best. On three unpublished demographic cells, neither approach beats reciting the national distribution. Population-marginal agreement is therefore evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.

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