Generating synthetic data through generative models is gaining interest in the ML
community and beyond. In the past, synthetic data was often regarded as a means to private …

Data-centric artificial intelligence (AI) is an emerging paradigm that emphasizes the critical
role of data in real-world machine learning (ML) systems—as a complement to model …

Deep generative models learn highly complex and non-linear representations to generate
realistic synthetic data. While they have achieved notable success in computer vision and …

Synthetic data serves as an alternative in training machine learning models, particularly
when real-world data is limited or inaccessible. However, ensuring that synthetic data …

In February 2024, Israel's Ministry of Health released microdata of live births in Israel in
2014. The dataset is based on Israel's National Registry of Live Births and offers substantial …

In this study, we explore the growing potential of AI and deep learning technologies,
particularly Generative Adversarial Networks (GANs) and Large Language Models (LLMs) …

PURPOSE There is strong interest from patients, researchers, the pharmaceutical industry,
medical journal editors, funders of research, and regulators in sharing clinical trial data for …

Advances in deep generative modelling have not translated well to tabular data. We argue
that this is caused by a mismatch in structure between popular generative models and …

Sharing data with third parties is essential for advancing science, but it is becoming more
and more difficult with the rise of data protection regulations, ethical restrictions, and growing …

The increasing use of machine learning in learning analytics (LA) has raised significant
concerns around algorithmic fairness and privacy. Synthetic data has emerged as a dual …