Under the Hood of Tabular Data Generation Models: Benchmarks with Extensive Tuning

The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. However, generating tabular data is not straightforward due to its heterogeneity, non-smooth distributions, complex depen...

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Veröffentlicht in:	arXiv.org 2024-12
Hauptverfasser:	G Charbel N Kindji, Rojas-Barahona, Lina Maria, Fromont, Elisa, Urvoy, Tanguy
Format:	Artikel
Sprache:	eng
Schlagworte:	Benchmarks Datasets Optimization Performance enhancement Tables (data) Tuning
Online-Zugang:	Volltext
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