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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
Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits
We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the …
Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito
引用
Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It
There has been a growing interest in off-policy evaluation in the literature such as recommender systems and personalized medicine. We …
Yuta Saito, Masahiro Nomura
引用 Proceedings
Long-term Off-Policy Evaluation and Learning
Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait …
Yuta Saito, Himan Abdollahpouri, Jesse Anderton, Ben Carterette, Mounia Lalmas
引用 コード arXiv Proceedings
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
We study off-policy evaluation (OPE) in slate contextual bandits where a policy selects multi-dimensional actions known as slates. This …
Haruka Kiyohara, Masahiro Nomura, Yuta Saito
引用 コード arXiv Proceedings
Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems
Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on …
Riku Togashi, Kenshi Abe, Yuta Saito
引用 動画 arXiv Proceedings
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation
Off-Policy Evaluation (OPE) aims to assess the effectiveness of counterfactual policies using only offline logged data and is often …
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito
引用 コード arXiv