2 papers
cs.LG2026
Coarse-to-Fine Learning of Dynamic Causal Structures
Dezhi Yang, Qiaoyu Tan, Carlotta Domeniconi +3
Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal…
cs.LG2024
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han +6
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks…