Biography
I’m a second-year Ph.D. student in Computer Science at Cornell University, where I’m fortunate to be advised by Prof. Thorsten Joachims. I completed my bachelor’s degree in Industrial Engineering and Economics at the Tokyo Institute of Technology.
My research lies at the intersection of machine learning and causal inference called counterfactual learning. I am interested in the counterfactual nature of logged bandit feedback and human behavior data obtained from interactive systems, and ways of using biased real-world datasets to assist safe and better decision making in the wild.
Download my CV here
Interests
- Counterfactual Evaluation
- Learning from Behavior Data
- Statistical Machine Learning
- Fairness in Ranking
Education
-
PhD in Computer Science, 2021 -
Cornell University
-
B.Eng in Industrial Engineering, 2016 - 2021
Tokyo Institute of Technology
Featured Publications
(2022).
Fair Ranking as Fair Division: Impact-Based Individual Fairness in Ranking.
In Proceedings of the 28th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (Acceptance rate=14.9%).
(2022).
Off-Policy Evaluation for Large Action Spaces via Embeddings.
In Proceedings of 39th International Conference on Machine Learning (ICML) (Acceptance rate=21.9%).
(2022).
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior Model.
In Proceedings of the 15th International Conference on Web Search and Data Mining (WSDM) (Acceptance rate=20.2%, Best Paper Runner-Up Award).
(2021).
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).
Optimal Off-Policy Evaluation from Multiple Logging Policies.
In Proceedings of 38th International Conference on Machine Learning (ICML) (Acceptance rate=21.5%).
(2020).
Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback.
In Proceedings of the 13th International Conference on Web Search and Data Mining (WSDM) (Acceptance rate=14.8%).
Recent Publications
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(2022).
Policy-Adaptive Estimator Selection for Off-Policy Evaluation.
In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (Acceptance rate=19.6%).
(2022).
Counterfactual Evaluation and Learning for Interactive Systems.
In Proceedings of the 28th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).
(2022).
Towards Resolving Propensity Contradiction in Offline Recommender Learning.
In Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI) (Acceptance rate=15%, Long Talk (top 4% of submissions)).
(2022).
Unbiased Recommender Learning from Biased Graded Implicit Feedback.
WSDM 2022 Workshop on Decision Making for Modern Information Retrieval System.
(2021).
A Real-World Implementation of Unbiased Lift-based Bidding System.
In Proceedings of the 2021 IEEE International Conference on Big Data (IEEE Big Data).
Awards
Paper Awards
- WSDM2022 Best Paper Runner-Up Award
- Paper: 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.
Outstanding Reviewer Awards
Other Awards
- Forbes Japan 30 Under 30 2022
- Japan Open Innovation Prize 2021 Prime Minister Award
Scholarships
- Masason Foundation Fellowship
- Funai Overseas Scholarship
- Doctoral fellowship by the Funai Foundation (a private foundation in Japan).
- Granted two full years of tuition plus a monthly stipend of $3,000.
Relevant Courses
Fall 2023
- CS6410: Advanced Systems
- CS6784: Machine Learning in Feedback Systems
Spring 2022
- ORIE6170: Engineering Societal Systems
Fall 2021
- CS6787: Advanced Machine Learning Systems
- CS7792: Fairness and Dynamics of Learning Systems
Academic Service
Conference Program Committee
- KDD: 2022, 2023
- AAAI: 2023
- ICLR: 2022
- WSDM: 2022, 2023
- NeurIPS: 2021, 2022
- ICML: 2021, 2022, 2023
- AISTATS: 2021
Workshop Organizer
Workshop Program Committee
- NeurIPS 2022 WS on Offline Reinforcement Learning
- NeurIPS 2022 WS on Reinforcement Learning for Real Life
- NeurIPS 2021 WS on Offline Reinforcement Learning
- NeurIPS 2021 WS on Causal Inference Challenges in Sequential Decision Making
Journal Reviewer
- ACM Transactions on Intelligent Systems and Technology (TIST)
- ACM Transactions on Information Systems (TOIS)
- ACM Transactions on Recommender Systems (TORS)
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
- Transactions on Machine Learning Research (TMLR)
Contact
- ys552 AT cornell.edu
- G33 Gates Hall, Cornell University, Ithaca, NY 14850