2 papers
cs.AI2026
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data
Fengxian Dong, Zhi Zheng, Xiao Han +5
Automated feature generation extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. Traditiona…
cs.LG2026
DAGAF: A directed acyclic generative adversarial framework for joint structure learning and tabular data synthesis
Hristo Petkov, Calum MacLellan, Feng Dong
Understanding the causal relationships between data variables can provide crucial insights into the construction of tabular datasets. Most existing causality learning methods typic…