4 papers
CASTANET: Causality-Aware Spatio-Temporal Adversarial Network Using Traffic Incident Effects
Toshiya Kitahara, Ryu Shirakami, Koh Takeuchi +1
Predicting non-periodic traffic congestion caused by sudden incidents (e.g., accidents and road damage) is crucial for advanced intelligent transportation systems. However, inciden…
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…
Online Policy Learning from Offline Preferences
Guoxi Zhang, Han Bao, Hisashi Kashima
In preference-based reinforcement learning (PbRL), a reward function is learned from a type of human feedback called preference. To expedite preference collection, recent works hav…
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…