Research Interests
My research lies at the intersection of statistical machine learning and causal inference called counterfactual learning. I am interested in the counterfactual nature of logged bandit feedback obtained from interactive systems, and ways of using biased real-world data to achieve safe and efficient automated decision making in the wild.  I am also working on fairness in recommender and ranking systems to develop a framework to improve long-term objectives of the platform by guaranteeing fair opportunities among item providers.
Education
2021 -
Tokyo Institute of Technology B.Eng. in Industrial Engineering and Economics
Publications
International Conference Proceedings (refereed) According to Google Scholar: h-index is 15 and total citation count is 960+. 1. Tatsuhiro Shimizu, Koichi Tanaka, Ren Kishimoto, Haruka Kiyohara, Masahiro Nomura, and Yuta Saito. Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits.  In  Proceedings of the 18th ACM Conference on Recommender Systems (RecSys), 2024 (to appear). 2.  Yuta Saito and Masahiro Nomura.  Hyperparameter Optimization Can Even be Harmful in Off-Policy Learning and How to Deal with It. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI), 2024. 3.  Yuta Saito , Himan Abdollahpouri, Jesse Anderton, Ben Carterette, and Mounia Lalmas.  Long-term Off-Policy Evaluation and Learning. In Proceedings of the ACM Web Conference (TheWebConf) , 2024. 4.  Riku Togashi, Kenshi Abe, and Yuta Saito .  Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. In Proceedings of the ACM Web Conference (TheWebConf), 2024. 5.  Haruka Kiyohara, Masahiro Nomura, and Yuta Saito. Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction. In Proceedings of the ACM Web Conference (TheWebConf), 2024. 6.   Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, and Yuta Saito. Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation. In Proceedings of the Twelfth International Conference on Learning Representations (ICLR), 2024. 7.  Haruka Kiyohara, Masatoshi Uehara, Yusuke Narita, Nobuyuki Shimizu, Yasuo Yamamoto, Yuta Saito. Off-Policy Evaluation of Ranking Policies under Diverse User Behavior. In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023. 8.  Yuta Saito , Qingyang Ren, and Thorsten Joachims.  Off-Policy Evaluation for Large Action Spaces via Conjunct Effect Modeling. In Proceedings of 40th International Conference on Machine Learning (ICML), 2023. 9.  Takuma Udagawa, Haruka Kiyohara, Yusuke Narita, Yuta Saito , and Kei Tateno.  Policy-Adaptive Es- timator Selection for Off-Policy Evaluation.  In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023 (Oral). 10.  Yuta Saito and Thorsten Joachims. Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking. In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2022. 11.  Yuta Saito and Thorsten Joachims.  Off-Policy Evaluation for Large Action Spaces via Embeddings.  In Proceedings of 39th International Conference on Machine Learning (ICML), 2022. 12. Yuta Saito and Masahiro Nomura.  Towards Resolving Propensity Contradiction in Offline Recom- mender Learning.  In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI), 2022 (Long Talk). 13. Haruka Kiyohara, Yuta Saito , Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, and Yasuo Ya- mamoto.  Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model. In Proceedings of International Conference on Web Search and Data Mining (WSDM), 2022 (Best Paper Runner-Up Award). 14.  Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa, Yuta Saito , and Akira Matsui. A Real-World Im- plementation of Unbiased Lift-based Bidding System.  In Proceedings of the 2021 IEEE International Conference on Big Data (BigData), 2021. 15. Yuta Saito , Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita.  Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation.  In Proceedings of the Neural Information Processing Systems (NeurIPS) Track on Datasets and Benchmarks, 2021. 16. Masahiro Nomura* and Yuta Saito* (* equal contribution). Efficient Hyperparameter Optimization un- der Multi-Source Covariate Shift. In Proceedings of the 30th ACM International Conference on Informa- tion and Knowledge Management (CIKM), 2021. 17.   Yuta Saito*, Takuma Udagawa*, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno (*equal contribution).  Evaluating the Robustness of Off-Policy Evaluation.  In  Proceedings of the 15th ACM Con- ference on Recommender Systems (RecSys), 2021. 18.  Nathan Kallus, Yuta Saito , and Masatoshi Uehara.  Optimal Off-Policy Evaluation from Multiple Log- ging Policies. In Proceedings of 38th International Conference on Machine Learning (ICML) , 2021. 19.  Yuta Saito .  Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions. In Proceedings of the 14th ACM Conference on Recommender Systems (RecSys), 2020. 20.  Yuta Saito.  Unbiased Pairwise Learning from Biased Implicit Feedback.  In Proceedings of 6th ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR), 2020. 21.  Yuta Saito and Shota Yasui.  Counterfactual Cross-Validation:  Stable Model Selection Procedure for Causal Inference Models. In Proceedings of 37th International Conference on Machine Learning (ICML), 2020. 22.  Yuta Saito .  Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In Pro- ceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020. 23.   Yuta Saito , Gota Morishita, and Shota Yasui.  Dual Learning Algorithm for Delayed Conversions.  In  Pro- ceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020 (short paper). 24.  Yuta Saito , Hayato Sakata, and Kazuhide Nakata.  Cost-Effective and Stable Policy Optimization Al- gorithm for Uplift Modeling with Multiple Treatments.  In Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), 2020. 25.  Yuta Saito , Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata.  Unbiased Recom- mender Learning from Missing-Not-At-Random Implicit Feedback. In Proceedings of the 13th Interna- tional Conference on Web Search and Data Mining (WSDM), 2020. 26.  Yuta Saito , Hayato Sakata, and Kazuhide Nakata.  Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data. In Proceedings of the 2019 SIAM International Confer- ence on Data Mining (SDM), 2019.
International Conference Workshop Papers (refereed)
1. Haruka Kiyohara, Kosuke Kawakami, and Yuta Saito . Accelerating Offline Reinforcement Learning Ap- plication in Real-Time Bidding and Recommendation: Potential Use of Simulation. RecSys 2021 Work- shop on Simulation Methods for Recommender Systems (SimuRec), 2021. 2.  Yuta Saito , Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita. A Large-scale Open Dataset for Bandit Algorithms.  RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions (REVEAL), 2020 (Oral Presentation). 3.  Yuta Saito , Takuma Udagawa, and Kei Tateno.  Data-Driven Off-Policy Estimator Selection: An Appli- cation in User Marketing on An Online Content Delivery Service. RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions (REVEAL), 2020. 4.  Yuta Saito , Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita.  A Large-scale Open Dataset for Bandit Algorithms.  ICML 2020 Workshop on Real World Experiment Design and Active Learning (RealML), 2020. 5.  Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa, Yuta Saito , and Akira Matsui.  Unbiased Lift- based Bidding System.  In Proceedings of the 2020 AdKDD&TargetAd Workshop, held in conjunction with the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (AdKDD), 2020.
Internships

Netflix Research Developed a method to learn a personalized reward function to optimize recommendations at Netflix Spotify Research Developed a statistical method to estimate and optimize the long-term value of policies from logged bandit data and put the method and results into a research paper (accepted at WWW'24)
Conference Tutorials
2022-09 2022-08 2021-09 CONSEQUENCES Workshop (at RecSys2022) (topic: off-policy evaluation in large action spaces) KDD2022 (topic: off-policy evaluation and learning) RecSys2021 (topic: off-policy evaluation and learning)
Invited Talks
2024-07
Sony Group Corporation (topic: counterfactual machine learning) Tsinghua University (topic: off-policy evaluation in large action spaces) Booking.com (topic: off-policy evaluation in large action spaces) Amazon (topic: off-policy evaluation in large action spaces) Instacart (topic: off-policy evaluation in large action spaces) Twitter (topic: off-policy evaluation in large action spaces) University of Amsterdam (topic: impact-based individual item fairness in ranking) 2021-06 2021-03 2021-03 2021-02 Tokyo Institute of Technology (topic: python package and open dataset for OPE research) Royal Melbourne Institute of Technology (topic: python package and open dataset for OPE research) California Institute of Technology (topic: python package and open dataset for OPE research) Cornell University (topic: python package and open dataset for OPE research)
Awards

TwoSigma Ph.D. Fellowship 2024 Runner-up (top 4 / 300+) RecSys 2023 Outstanding Reviewer Award Forbes Japan 30 Under 30 2022 WSDM 2022 Best Paper Runner-Up Award NeurIPS 2021 Outstanding Reviewer Award
Scholarships
2021 - 2023    Funai Overseas Scholarship Doctoral research fellowship by the Funai Foundation (a private foundation in Japan). Granted two full years of tuition plus a monthly stipend of $3,000 for living expenses.
Involved Research Projects
•  Open Bandit Project (https://github.com/st-tech/zr-obp) Open Bandit Project is an open-source research project that aims to enable realistic and reproducible experiments on bandit algorithms and their off-policy evaluation. The project consists of a large-scale real-world dataset called Open Bandit Dataset and Python software called Open Bandit Pipeline. The project was awarded The Prime Minister's Award for Open Innovation by the Japanese Government.
Teaching Assistant
Fall 2023 Advanced Machine Learning (CS6780, Cornell)
Professional Service
Conference Program Committee

Workshop Organizer
- RecSys 2023 Workshop on Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems - RecSys 2022 Workshop on Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning
Workshop Program Committee
- NeurIPS 2022 Workshop on Offline Reinforcement Learning - NeurIPS 2022 Workshop on Reinforcement Learning for Real Life - NeurIPS 2021 Workshop on Offline Reinforcement Learning - NeurIPS 2021 Workshop on Causal Inference Challenges in Sequential Decision Making
Journal Reviewer
- IEEE Transactions on Signal Processing - IEEE Transactions on Knowledge and Data Engineering (TKDE) - ACM Transactions on Intelligent Systems and Technology (TIST) - ACM Transactions on Information Systems (TOIS) - ACM Transactions on Recommender Systems (TORS) - Transactions on Machine Learning Research (TMLR)
Languages
Japanese (native), English (TOEFL iBT: 105)