Publications

• Ren Kishimoto*, Rikiya Takehi*, Koichi Tanaka, Riku Togashi, Masahiro Nomura, Yoji Tomita, Yuta Saito.
  Beyond Match Maximization and Fairness: Retention-Optimized Two-Sided Matching.
  In Proceedings of the 12th International Conference on Learning Representations (ICLR), 2025.

• Koichi Tanaka*, Ren Kishimoto*, Bushun Kawagishi, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito.
  Off-Policy Learning with Limited Supply.
  In Proceedings of the ACM Web Conference (TheWebConf), 2025.

• Ren Kishimoto, Tatsuhiro Shimizu, Kazuki Kawamura, Takanori Muroi, Yusuke Narita, Yuki Sasamoto, Kei Tateno, Takuma Udagawa, Yuta Saito.
  Offline Contextual Bandits in the Presence of New Actions.
  KDD 2025 Workshop - Causal Inference and Machine Learning in Practice.

• Tatsuhiro Shimizu*, Koichi Tanaka*, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, Yuta Saito.
  Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits.
  In Proceedings of the 18th ACM Recommender Systems Conference (RecSys), 2024.

• Ren Kishimoto*, Koichi Tanaka*, Haruka Kiyohara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito.
  Efficient Offline Learning of Ranking Policies via Top-k Policy Decomposition.
  ICML 2024 Workshop on Aligning Reinforcement Learning Experimentalists and Theorists (ARLET), 2024.

• Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito.
  Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation.
  In Proceedings of the 12th International Conference on Learning Representations (ICLR), 2024.

• Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, and Yuta Saito.
  SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy Evaluation.
  arXiv preprint, 2023.