Saito, Yuta

15 publications

ICLR 2025 A General Framework for Off-Policy Learning with Partially-Observed Reward Rikiya Takehi, Masahiro Asami, Kosuke Kawakami, Yuta Saito

ICLR 2025 Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits Yuta Natsubori, Masataka Ushiku, Yuta Saito

NeurIPS 2025 MultiScale Contextual Bandits for Long Term Objectives Richa Rastogi, Yuta Saito, Thorsten Joachims

ICLR 2025 POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition Yuta Saito, Jihan Yao, Thorsten Joachims

ICMLW 2024 Efficient Offline Learning of Ranking Policies via Top-$k$ Policy Decomposition Ren Kishimoto, Koichi Tanaka, Haruka Kiyohara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito

IJCAI 2024 Hyperparameter Optimization Can Even Be Harmful in Off-Policy Learning and How to Deal with It Yuta Saito, Masahiro Nomura

ICMLW 2024 MultiScale Policy Learning for Alignment with Long Term Objectives Richa Rastogi, Yuta Saito, Thorsten Joachims

ICLRW 2024 Prompt Optimization with Logged Bandit Data Haruka Kiyohara, Yuta Saito, Daniel Yiming Cao, Thorsten Joachims

ICLR 2024 Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito

ICML 2023 Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling Yuta Saito, Qingyang Ren, Thorsten Joachims

AAAI 2023 Policy-Adaptive Estimator Selection for Off-Policy Evaluation Takuma Udagawa, Haruka Kiyohara, Yusuke Narita, Yuta Saito, Kei Tateno

ICML 2022 Off-Policy Evaluation for Large Action Spaces via Embeddings Yuta Saito, Thorsten Joachims

IJCAI 2022 Towards Resolving Propensity Contradiction in Offline Recommender Learning Yuta Saito, Masahiro Nomura

ICML 2021 Optimal Off-Policy Evaluation from Multiple Logging Policies Nathan Kallus, Yuta Saito, Masatoshi Uehara

ICML 2020 Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models Yuta Saito, Shota Yasui