1 citations · 2 across the 5 of their papers we have counts for
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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…
Improving Predictions on Highly Unbalanced Data Using Open Source Synthetic Data Upsampling
Ivona Krchova, Michael Platzer, Paul Tiwald
Unbalanced tabular data sets present significant challenges for predictive modeling and data analysis across a wide range of applications. In many real-world scenarios, such as fra…
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…
Strong statistical parity through fair synthetic data
Ivona Krchova, Michael Platzer, Paul Tiwald
AI-generated synthetic data, in addition to protecting the privacy of original data sets, allows users and data consumers to tailor data to their needs. This paper explores the cre…
Rule-adhering synthetic data -- the lingua franca of learning
Michael Platzer, Ivona Krchova
AI-generated synthetic data allows to distill the general patterns of existing data, that can then be shared safely as granular-level representative, yet novel data samples within…