5 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
ITSY: Causal Discovery From Irregular Time-Series Data
Wenbo Xu, Yue He, Yunhai Wang +2
Structural causal models for time series recover contemporaneous and lagged effects, but most methods require complete observation windows and become misspecified when samples are…
A Knowledge-Informed Pretrained Model for Causal Discovery
Wenbo Xu, Yue He, Yunhai Wang +4
Causal discovery has been widely studied, yet many existing methods rely on strong assumptions or fall into two extremes: either depending on costly interventional signals or parti…
Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection
Chunyu Wei, Siyuan He, Yu Wang +7
Graph anomaly detection (GAD) is crucial in applications like fraud detection and cybersecurity. Despite recent advancements using graph neural networks (GNNs), two major challenge…
Beyond the Pre-Service Horizon: Infusing In-Service Behavior for Improved Financial Risk Forecasting
Senhao Liu, Zhiyu Guo, Zhiyuan Ji +5
Typical financial risk management involves distinct phases for pre-service risk assessment and in-service default detection, often modeled separately. This paper proposes a novel f…
Graph Evidential Learning for Anomaly Detection
Chunyu Wei, Wenji Hu, Xingjia Hao +4
Graph anomaly detection faces significant challenges due to the scarcity of reliable anomaly-labeled datasets, driving the development of unsupervised methods. Graph autoencoders (…