Learning policies for decision-making, such as recommending treatments in clinical settings,
is important for enhancing clinical decision-support systems. However, the challenge lies in …

Contextual bandit and reinforcement learning algorithms have been successfully used in
various interactive learning systems such as online advertising, recommender systems, and …

Reinforcement learning has recently become an active topic in recommender system
research, where the logged data that records interactions between items and users …

Unlike traditional supervised learning, in many settings only partial feedback is available.
We may only observe outcomes for the chosen actions, but not the counterfactual outcomes …

Off-policy learning (OPL) often involves minimizing a risk estimator based on importance
weighting to correct bias from the logging policy used to collect data. However, this method …