mlsanityData to 13 Oct 2026

Synthcity: facilitating innovative use cases of synthetic data in different data modalities

arXiv 2301.07573 ↗First posted 18 Jan 2023cs.LG cs.AIcited by 76

Not on the map. Why?

Abstract (arXiv)

Synthcity is an open-source software package for innovative use cases of synthetic data in ML fairness, privacy and augmentation across diverse tabular data modalities, including static data, regular and irregular time series, data with censoring, multi-source data, composite data, and more. Synthcity provides the practitioners with a single access point to cutting edge research and tools in synthetic data. It also offers the community a playground for rapid experimentation and prototyping, a one-stop-shop for SOTA benchmarks, and an opportunity for extending research impact. The library can be accessed on GitHub (https://github.com/vanderschaarlab/synthcity) and pip (https://pypi.org/project/synthcity/). We warmly invite the community to join the development effort by providing feedback, reporting bugs, and contributing code.

Key facts. Cited by 76 papers in the map (data through 13 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 18 Jan 2023.

Builds on

17 of 37 references.

References · 17

  1. 2016Deep Learning with Differential Privacycited by 2,718
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,273
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,915
  4. 2017Counterfactual Fairnesscited by 804
  5. 2017Data Augmentation Generative Adversarial Networkscited by 278
  6. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 252
  7. 2018Gender Bias in Neural Natural Language Processingcited by 211
  8. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  9. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  10. 2022HyperImpute: Generalized Iterative Imputation with Automatic Model Selectioncited by 81
  11. 2022Synthetic Data -- what, why and how?cited by 66
  12. 2021Stereotype and Skew: Quantifying Gender Bias in Pre-trained and Fine-tuned Language Modelscited by 57
5 more
  1. 2019G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminatorscited by 53
  2. 2021Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Datacited by 18
  3. 2022Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populationscited by 14
  4. 2022Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networkscited by 11
  5. 2018RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networkscited by 10

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 18; 2024: 14; 2025: 28; 2026: 16.

76 papers.

Sort:

By area:

  1. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  2. 2023Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Datacited by 29
  3. 2023Synthetic data, real errors: how (not) to publish and use synthetic datacited by 29
  4. 2024SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Datacited by 27
  5. 2024TabPFGen -- Tabular Data Generation with TabPFNcited by 26
  6. 2023Reimagining Synthetic Tabular Data Generation through Data-Centric AI: A Comprehensive Benchmarkcited by 23
  7. 2023Interpretable Deep Clustering for Tabular Datacited by 19
  8. 2024Systematic Assessment of Tabular Data Synthesiscited by 18
  9. 2023TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Seriescited by 17
  10. 2023Continuous Diffusion for Mixed-Type Tabular Datacited by 17
  11. 2024HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protectioncited by 16
  12. 2024TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Modelscited by 15
38 more
  1. 2023Auditing and Generating Synthetic Data with Controllable Trust Trade-offscited by 13
  2. 2023Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utilitycited by 13
  3. 2025TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Datacited by 13
  4. 2023On the Usefulness of Synthetic Tabular Data Generationcited by 12
  5. 2024Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Modelscited by 12
  6. 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measurescited by 9
  7. 2024Generating Realistic Tabular Data with Large Language Modelscited by 9
  8. 2025Synthetic Tabular Data: Methods, Attacks and Defensescited by 9
  9. 2024Navigating Tabular Data Synthesis Research: Understanding User Needs and Tool Capabilitiescited by 8
  10. 2023Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesiscited by 7
  11. 2024Benchmarking the Fidelity and Utility of Synthetic Relational Datacited by 7
  12. 2025SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domainscited by 7
  13. 2023Diverse Community Data for Benchmarking Data Privacy Algorithmscited by 6
  14. 2024Synthetic Data: Revisiting the Privacy-Utility Trade-offcited by 6
  15. 2023The Real Deal Behind the Artificial Appeal: Inferential Utility of Tabular Synthetic Datacited by 5
  16. 2025Privacy Auditing Synthetic Data Release through Local Likelihood Attackscited by 5
  17. 2025Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithmscited by 4
  18. 2025SampleLLM: Optimizing Tabular Data Synthesis in Recommendationscited by 4
  19. 2025Tabby: A Language Model Architecture for Tabular and Structured Data Synthesiscited by 4
  20. 2025Diffusion Transformers for Tabular Data Time Series Generationcited by 4
  21. 2025Ensembling Membership Inference Attacks Against Tabular Generative Modelscited by 4
  22. 2025Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesiscited by 4
  23. 2025Large Language Models for Imbalanced Classification: Diversity makes the differencecited by 4
  24. 2025Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inferencecited by 4
  25. 2026Cascaded Flow Matching for Heterogeneous Tabular Data with Mixed-Type Featurescited by 4
  26. 2025Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generationcited by 3
  27. 2025Synthetic Data Generation for Augmenting Small Samplescited by 3
  28. 2025Adversarial Causal Tuning for Realistic Time-series Generationcited by 3
  29. 2025TAGAL: Tabular Data Generation using Agentic LLM Methodscited by 3
  30. 2025When Tables Leak: Attacking String Memorization in LLM-Based Tabular Data Generationcited by 3
  31. 2023Tertiary Lymphoid Structures Generation through Graph-based Diffusioncited by 2
  32. 2024Debiasing Synthetic Data Generated by Deep Generative Modelscited by 2
  33. 2023A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functionscited by 1
  34. 2023Methods for generating and evaluating synthetic longitudinal patient data: a systematic reviewcited by 1
  35. 2025Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distancecited by 1
  36. 2025A Generalized Theory of Mixup for Structure-Preserving Synthetic Datacited by 1
  37. 2025What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Modelscited by 1
  38. 2025SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generationcited by 1

50 most cited of 76 All 76 (JSON)