PFN-Syn achieves the highest average downstream utility among evaluated synthetic generators, with higher generation throughput than training-based baselines, while maintaining competitive distributional similarity and distance-to-closest-record privacy behavior.
Synthetic tabular data is increasingly used to support data sharing and machine learning development, where access to real data is restricted or sensitive. However, existing tabular data generators often require dataset-specific training or fine-tuning, data preprocessing, and extensive hyperparameter tuning to achieve...
Hadi Koubeissy, Michel El Khoury, Marc Kamradt et al.· Annual International Compute...· 0 citations
This work discusses key considerations and methods for generating synthetic data, including sequential modeling, differentially private synthesis, deep generative models, and large language models, and outlines some open research challenges and future directions for synthetic data development.
Yinyihong Liu, Jerome P. Reiter· Annual Review of Statistics...· 0 citations
This work considers normalized histograms as distribution estimators and characterize the asymptotically optimal domain-aware privacy mechanism within a specific class of DP mechanisms, and introduces PubMix, a public-data-aware DP mechanism that can be used in histogram-based data synthesis pipelines.
Sajani Vithana, Sangwon Jung, Haoyang Hu et al.· arXiv.org· 0 citations
A comprehensive survey and analysis of synthetic data generation techniques, classifying them by data modality, generation method, and application purpose and exploring how synthetic data contributes to the resilience of ML pipelines against failure modes such as concept drift, noise, and adversarial attacks.
Dennis M. Ritchie, Allen Newell· International Journal of Dat...· 0 citations
This work demonstrates that PFNs, which are originally trained for classification, successfully outperform classification baselines on ranking data and introduces a novel sampling and inference scheme to obtain pairwise predictions from PFNs' native pointwise architecture, analogous to pairwise LTR.
David Vos, Samarth Bhargav, M. D. Rijke et al.· International Conference on...· 0 citations
SynPre-FL is proposed, a unified framework combining high-fidelity synthetic EHR generation with synthetic-pretrained FL for robust prediction under non-IID conditions and provides a practical and reproducible framework to enable privacy-aware, interpretable, and robust clinical prediction from distributed tabular EHR...
Akarsh K. Nair, Muhammad Arifur Rahman, N. Shopland et al.· arXiv.org· 0 citations
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