DAGAF is a directed acyclic generative adversarial framework that jointly learns structure and generates synthetic tabular data. The approach combines structure learning with adversarial synthesis, addressing a key gap in synthetic data generation for structured datasets.
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DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis
DAGAF uses adversarial training to simultaneously learn data structure and generate synthetic tabular records, eliminating the need for pre-specified relationships in synthetic data generation.
Tuesday, April 7, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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