Record link: 
http://hdl.handle.net/20.500.12708/231117
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Title: 
A Survey on Deep Learning Approaches for Tabular Data Generation:Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond
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Citation: 
Stoian, M. C., Giunchiglia, E., & Lukasiewicz, T. (2026). A Survey on Deep Learning Approaches for Tabular Data Generation:Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond. Transactions on Machine Learning Research, 02, 1–26.
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Stoian, Mihaela C. 
Giunchiglia, Eleonora 
Lukasiewicz, Thomas 
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Organisational Unit: 
E192-07 - Forschungsbereich Artificial Intelligence Techniques 
E192-03 - Forschungsbereich Knowledge Based Systems 
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Journal: 
Transactions on Machine Learning Research 
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Date (published): 
2026
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Number of Pages: 
26
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Publisher: 
Transactions on Machine Learning Research
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Peer reviewed: 
Yes
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Keywords: 
Tabular data generation; Deep Generative Models; Requirement-aware data synthesis; Synthetic data evaluation
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Research Areas: 
Information Systems Engineering: 100%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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