1 citations · 1 across the 2 of their papers we have counts for
3 papers
Democratizing Tabular Data Access with an Open$\unicode{x2013}$Source Synthetic$\unicode{x2013}$Data SDK
Ivona Krchova, Mariana Vargas Vieyra, Mario Scriminaci +1
Machine learning development critically depends on access to high-quality data. However, increasing restrictions due to privacy, proprietary interests, and ethical concerns have cr…
Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
Andrey Sidorenko, Michael Platzer, Mario Scriminaci +1
Evaluating the quality of synthetic data remains a key challenge for ensuring privacy and utility in data-driven research. In this work, we present an evaluation framework that qua…
TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
Paul Tiwald, Ivona Krchova, Andrey Sidorenko +3
Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications. We introduce the Tabular Auto-Regr…