7 citations · 9 across the 5 of their papers we have counts for
5 papers
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…
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…
Causality Learning With Wasserstein Generative Adversarial Networks
Hristo Petkov, Colin Hanley, Feng Dong
Conventional methods for causal structure learning from data face significant challenges due to combinatorial search space. Recently, the problem has been formulated into a continu…
Hyper-Learning for Gradient-Based Batch Size Adaptation
Calum Robert MacLellan, Feng Dong
Scheduling the batch size to increase is an effective strategy to control gradient noise when training deep neural networks. Current approaches implement scheduling heuristics that…
DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks
Hristo Petkov, Colin Hanley, Feng Dong
The combinatorial search space presents a significant challenge to learning causality from data. Recently, the problem has been formulated into a continuous optimization framework…