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cs.LG2026
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…
cs.LG2024
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…
cs.LG2023
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…