mlsanityData to 7 Oct 2026

Permutation-Invariant Tabular Data Synthesis

arXiv 2211.09286 ↗First posted 17 Nov 2022cs.LGcited by 6

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Abstract (arXiv)

Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first conduct an extensive empirical study to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find the suitable column order of input data for CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 19 of its 43 references that are in the map. First posted on arXiv 17 Nov 2022.

Builds on

19 of 43 references.

References · 19

  1. 2022Generative Adversarial Networkscited by 5,603
  2. 2017Improved Training of Wasserstein GANscited by 2,223
  3. 2018On the Spectral Bias of Neural Networkscited by 1,321
  4. 2019Modeling Tabular data using Conditional GANcited by 695
  5. 2021Deep Neural Networks and Tabular Data: A Surveycited by 489
  6. 2017Generating Multi-label Discrete Patient Records using Generative Adversarial Networkscited by 197
  7. 2017Learning Steerable Filters for Rotation Equivariant CNNscited by 193
  8. 2018Data Synthesis based on Generative Adversarial Networkscited by 184
  9. 2021CTAB-GAN: Effective Table Data Synthesizingcited by 156
  10. 2020On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Locationcited by 141
  11. 2018Recent Advances in Autoencoder-Based Representation Learningcited by 130
  12. 2018Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networkscited by 129
7 more
  1. 2022CTAB-GAN+: Enhancing Tabular Data Synthesiscited by 70
  2. 2020Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learningcited by 67
  3. 2017Quantifying Translation-Invariance in Convolutional Neural Networkscited by 30
  4. 2019Advancements in Image Classification using Convolutional Neural Networkcited by 20
  5. 2020SYNC: A Copula based Framework for Generating Synthetic Data from Aggregated Sourcescited by 16
  6. 2020Regularizing Towards Permutation Invariance in Recurrent Modelscited by 13
  7. 2017Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariancecited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.

6 papers.

  1. 2023TabuLa: Harnessing Language Models for Tabular Data Synthesiscited by 42
  2. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  3. 2025Masked Diffusion Models are Secretly Learned-Order Autoregressive Modelscited by 3
  4. 2025Toward Real-World Table Agents: Capabilities, Workflows, and Design Principles for LLM-based Table Intelligencecited by 2
  5. 2026Multimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attentioncited by 1
  6. 2026On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metricscited by 0