2025

Off-Policy Evaluation and Learning for the Future under Non-Stationarity
Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), …
Tatsuhiro Shimizu, Kazuki Kawamura, Takanori Muroi, Yusuke Narita, Kei Tateno, Takuma Udagawa, Yuta Saito
引用 OpenReview
A General Framework for Off-Policy Learning with Partially-Observed Reward
Off-policy learning (OPL) in contextual bandits aims to learn a decision-making policy that maximizes the target rewards by using only …
Rikiya Takehi, Masahiro Asami, Kosuke Kawakami, Yuta Saito
引用 OpenReview
Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits
Off-Policy Evaluation and Learning (OPE/L) in contextual bandits is rapidly gaining popularity in real systems because new policies can …
Yuta Natsubori, Masataka Ushiku, Yuta Saito
引用 OpenReview
POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition
We study off-policy learning (OPL) of contextual bandit policies in large discrete action spaces where existing methods – most of …
Yuta Saito, Jihan Yao, Thorsten Joachims
引用 OpenReview