4 papers
Treatment Effect Estimation with Differentiated Networked Effect on Graph Data
Xiaofeng Lin, Han Bao, Hisashi Kashima
Estimating individual treatment effect (ITE) from observational graph data is crucial for decision-making in the fields such as commerce and medicine. This task is challenging due…
Robust Anomaly Detection Under Normality Distribution Shift in Dynamic Graphs
Xiaoyang Xu, Xiaofeng Lin, Koh Takeuchi +2
Anomaly detection in dynamic graphs is a critical task with broad real-world applications, including social networks, e-commerce, and cybersecurity. Most existing methods assume th…
Evaluating Saliency Explanations in NLP by Crowdsourcing
Xiaotian Lu, Jiyi Li, Zhen Wan +3
Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to expla…
Estimating Treatment Effects Under Heterogeneous Interference
Xiaofeng Lin, Guoxi Zhang, Xiaotian Lu +3
Treatment effect estimation can assist in effective decision-making in e-commerce, medicine, and education. One popular application of this estimation lies in the prediction of the…