arXiv AI

TDGT: A Tabular Data Generation Toolkit supporting adaptive GPU-accelerated Bayesian mixture models, diffusion-based models, and latent-space generative modeling

arXiv:2606. 31268v1 Announce Type: cross Abstract: The growing demand for privacy-preserving data sharing has positioned synthetic data generation as a critical component of responsible AI workflows.

arXiv AI

2d ago

Generating Benchmark Health Data Using a Tabular Diffusion Transformer

arXiv:2608. 14496v1 Announce Type: cross Abstract: Cross-Tabular Data Generation (CTDG) seeks to learn a generative model from multiple heterogeneous tables and produce new synthetic tabular datasets.

By Hao Yan, Lisa Pilgram, Dan Liu, Linglong Kong, Fida Dankar, Khaled El Emam

llms diffusion benchmarks

arXiv AI

Jul 23

SynPre-FL: Synthetic data-driven pretraining integrated Federated Learning training framework

arXiv:2607. 19524v1 Announce Type: cross Abstract: Federated learning (FL) offers a promising approach to privacy-preserving clinical risk prediction, but its deployment remains limited by restricted data sharing, client heterogeneity, class imbalance, and the lack of realistic tabular electronic health record (EHR) benchmarks.

By Akarsh K Nair, Muhammad Arifur Rahman, Nicholas Shopland, Andy Burton, Jun He, Yuan Shen, David Baldwin, Emma O'Dowd, Amna Burzic, Mufti Mahmud, David J. Brown

diffusion benchmarks safety

arXiv Machine Learning

Jun 19

Variational Consensus Monte Carlo for Bayesian Mixture

arXiv:2606. 19643v1 Announce Type: cross Abstract: Motivated by the privacy, sensitivity and sharing limitations of health data, we present a comprehensive pipeline for inference of Bayesian mixture models within a federated learning setting, i.

By Julie Fendler, Francesca L. Crowe, Tom Marshall, Sylvia Richardson, Paul D. W. Kirk

benchmarks safety

arXiv AI

Aug 11

A Sobering Look at Tabular Data Generation via Probabilistic Circuits

arXiv:2603. 23016v2 Announce Type: replace-cross Abstract: Tabular data is more challenging to generate than text and images, due to its heterogeneous features and much lower sample sizes.

By Davide Scassola, Dylan Ponsford, Adri\'an Javaloy, Sebastiano Saccani, Luca Bortolussi, Henry Gouk, Antonio Vergari

diffusion benchmarks

arXiv Machine Learning

Jul 3

eXact-Prior Variational Autoencoder (X-VAE): Learning Data-Adaptive Gaussian Mixture Priors for Latent Distributions

arXiv:2607. 01275v1 Announce Type: cross Abstract: Variational Autoencoders (VAEs) commonly assume a standard isotropic Gaussian prior over the latent space, an assumption that often fails to capture the true distribution of latent representations for complex datasets.

By Qijun Chen, Shaofan Li

diffusion benchmarks

arXiv Machine Learning

Jul 28

PYPM-GGD: Pitman-Yor Process Mixture with Generalized Gaussian Density using ADAM

arXiv:2607. 24583v1 Announce Type: new Abstract: Large scale Bayesian nonparametrics (BNP) learner such as Stochastic Variational Inference (SVI) can handle datasets with large class number and large training size at fractional cost.

By Kart-Leong Lim

benchmarks

arXiv AI

Aug 11

GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

arXiv:2608. 07881v1 Announce Type: new Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols.

By Zihua Yang, Zhencheng Xie, Junyang Chen, Liang Xie, Yiqun Zhang, Mengke Li, Yang Lu

llms rag safety

arXiv AI

Jun 9

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

arXiv:2606. 09257v1 Announce Type: cross Abstract: High-Dimensional Low-Sample Size (HDLSS) tabular domains (e.

By Al Zadid Sultan Bin Habib, Md Younus Ahamed, Prashnna Gyawali, Gianfranco Doretto, Donald A. Adjeroh

diffusion benchmarks

arXiv Machine Learning

Aug 11

Quantifying Membership Disclosure Risk for Tabular Synthetic Data Using Kernel Density Estimators

arXiv:2603. 10937v2 Announce Type: replace Abstract: The use of synthetic data has become increasingly popular as a privacy-preserving alternative to sharing real datasets, especially in sensitive domains such as healthcare, finance, and demography.

By Rajdeep Pathak, Amit Basak, Sayantee Jana

safety

arXiv AI

Jun 18

PSyGenTAB: A Privacy-Preserving Framework for Synthetic Clinical Tabular Data Generation via Constrained Optimization

arXiv:2606. 18518v1 Announce Type: cross Abstract: The development of medical AI is constrained by limited access to high-quality clinical data due to institutional silos and strict privacy regulations such as HIPAA and GDPR.

By Arshia Ilaty, Hossein Shirazi, Manasi Chitale, Kedar Hegde, Dhanalakshmi Ramesh, Rashmi S. Manjunath, Amir Rahmani, Hajar Homayouni

rag benchmarks safety

arXiv Machine Learning

Jul 7

Memisis: Orchestrating and Evaluating Synthetic Data for Tabular Health Datasets

arXiv:2605. 17758v2 Announce Type: replace Abstract: Synthetic data is widely used in healthcare to create datasets that preserve statistical properties of real data without exposing sensitive patient information.

By Nitish Nagesh, Pengbao Zhou, Atchuth Naveen Chilaparasetti, Yajat Nagaraj Kiran, Tu Nguyen, Arshia Harish Puthran, Muhjaazee Love, Aadi Sharma, Mahdi Bagheri, Ian Harris, Amir M. Rahmani

llms agents diffusion benchmarks safety

arXiv Machine Learning

Aug 12

Smooth Flow Matching for Synthesizing Functional Data

arXiv:2508. 13831v4 Announce Type: replace-cross Abstract: Functional data, i.

By Jianbin Tan, Anru R. Zhang

diffusion safety data-engineering

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