Yuta Saito
Address
EmailWebsite
GitHubLast Updated

Ithaca, NY, USA
ys552@cornell.edu
https://usait0.com/en/
https://github.com/usaito
August 21, 2022

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 automated decision making in the wild.

Education
2021 -Cornell UniversityPh.D. Student in Computer Science
Research Field: Counterfactual Evaluation and Learning, Fairness in Ranking

2016 2021

Tokyo Institute of Technology
B.Eng. in Industrial Engineering and Economics
Research Field: Counterfactual Inference, Oﬀ-Policy Evaluation, Information Retrieval

Publications
International Conference Proceedings (refereed)
1. Yuta Saito and Thorsten Joachims. Fair Ranking as Fair Division: Impact-Based Individual Fairness inRanking. In Proceedings of the 28th ACM SIGKDD International Conference on Knowledge Discovery andData Mining (KDD), 2022.
2. Yuta Saito and Thorsten Joachims. Oﬀ-Policy Evaluation for Large Action Spaces via Embeddings. InProceedings of 39th International Conference on Machine Learning (ICML), 2022.
3. Yuta Saito and Masahiro Nomura. Towards Resolving Propensity Contradiction in Oﬄine Recommender Learning. In Proceedings of the 31st International Joint Conference on Artiﬁcial Intelligence(IJCAI), 2022 (to appear, Long Talk).
4. Haruka Kiyohara, Yuta Saito, Tatsuya Matsuhiro, Yusuke Narita, Nobuyuki Shimizu, and Yasuo Yamamoto. 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 PaperRunner-Up Award).
5. Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa, Yuta Saito, and Akira Matsui. A Real-World Implementation of Unbiased Lift-based Bidding System. In Proceedings of the 2021 IEEE International
Conference on Big Data (BigData), 2021.
6. 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.
7. Masahiro Nomura∗ and Yuta Saito∗ (∗ equal contribution). Eﬃcient Hyperparameter Optimization under Multi-Source Covariate Shift. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM), 2021.

8. Yuta Saito∗ , Takuma Udagawa∗ , Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno (∗ equalcontribution). Evaluating the Robustness of Oﬀ-Policy Evaluation. In Proceedings of the 15th ACM Conference on Recommender Systems (RecSys), 2021.
9. Nathan Kallus, Yuta Saito, and Masatoshi Uehara. Optimal Oﬀ-Policy Evaluation from Multiple Logging Policies. In Proceedings of 38th International Conference on Machine Learning (ICML), 2021.
10. Yuta Saito. Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions. In Proceedingsof the 14th ACM Conference on Recommender Systems (RecSys), 2020.
11. 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.
12. 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.
13. Yuta Saito. Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR), 2020.
14. Yuta Saito, Gota Morishita, and Shota Yasui. Dual Learning Algorithm for Delayed Conversions. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information
Retrieval (SIGIR), 2020 (short paper).
15. Yuta Saito, Hayato Sakata, and Kazuhide Nakata. Cost-Eﬀective and Stable Policy Optimization Algorithm for Uplift Modeling with Multiple Treatments. In Proceedings of the 2020 SIAM International
Conference on Data Mining (SDM), 2020.
16. Yuta Saito, Suguru Yaginuma, Yuta Nishino, Hayato Sakata, and Kazuhide Nakata. Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback. In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM), 2020.
17. 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 Conference on Data Mining (SDM), 2019.

International Conference Workshop Papers (refereed)
1. Haruka Kiyohara, Kosuke Kawakami, and Yuta Saito. Accelerating Oﬄine Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation. RecSys 2021 Workshop on Simulation Methods for Recommender Systems (SimuRec), 2021.
2. Yuta Saito, Shunsuke Aihara, Megumi Matsutani, and Yusuke Narita. A Large-scale Open Dataset forBandit 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 Application in User Marketing on An Online Content Delivery Service. RecSys 2020 Workshop on Bandit andReinforcement 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 Liftbased 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.

International Conference Tutorial/Workshop Proposals
1. Yuta Saito and Thorsten Joachims. Counterfactual Evaluation and Learning for Recommender Systems: Foundations, Implementations, and Recent Advances. In Proceedings of the 28th ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining (KDD), 2022.
2. Olivier Jeunen, Thorsten Joachims, Harrie Oosterhuis, Yuta Saito, and Flavian Vasile. CONSEQUENCES– Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems. In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), 2022 (to appear).
3. Yuta Saito and Thorsten Joachims. Counterfactual Evaluation and Learning for Recommender Systems: Foundations, Implementations, and Recent Advances. In Proceedings of the 15th ACM Conference
on Recommender Systems (RecSys), 2021.

Awards
2022
2022
2021

Forbes Japan 30 Under 30 2022
WSDM 2022 Best Paper Runner-Up Award
NeurIPS 2021 Outstanding Reviewer Award

Scholarships
2022 Masason Foundation Fellowship
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. Awarded
The Prime Minister’s Award for Open Innovation by the Japanese Government.

Professional Service
Conference Program Committee- KDD- AAAI- ICLR- WSDM- NeurIPS- ICML- AISTATS

2022
2023
2022
2022, 2023
2021, 2022
2021, 2022
2021

Workshop Organizer- RecSys 2022 Workshop on Causality, Counterfactuals, Sequential Decision-Making & Reinforcement Learning

Workshop Program Committee- NeurIPS 2022 Workshop on Reinforcement Learning for Real Life - NeurIPS 2021 Workshop on Oﬄine Reinforcement Learn- NeurIPS 2021 Workshop on Causal Inference Challenges in Sequential Decision Making

Journal Reviewer- IEEE Transactions on Knowledge and Data Engineering (TKDE)- ACM Transactions on Intelligent Systems and Technology (TIST)- ACM Transactions on Information Systems (TOIS)- Transactions on Machine Learning Research (TMLR)

LanguagesJapanese (native), English (TOEFL iBT: 105)