Yuta Saito

Yuta Saito

Third-year CS Ph.D. Student

Cornell University

Biography

I’m a third-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

Recent & Upcoming Talks

Counterfactual Tutorial at KDD2022
KDD2022 Tutorial on Counterfactual Evaluation and Learning
Counterfactual Tutorial at RecSys2021
RecSys2021 Tutorial on Counterfactual Evaluation and Learning

Recent Publications

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(2024). Effective Off-Policy Evaluation and Learning in Contextual Combinatorial Bandits. In Proceedings of the 18th ACM Conference on Recommender Systems (RecSys).

Cite

(2024). 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) (Acceptance rate=15%).

Cite Proceedings

(2024). Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction. In Proceedings of the ACM Web Conference 2024 (TheWebConf) (Acceptance Rate=20.2%).

Cite Code arXiv Proceedings

(2024). Scalable and Provably Fair Exposure Control for Large-Scale Recommender Systems. In Proceedings of the ACM Web Conference 2024 (TheWebConf) (Acceptance Rate=20.2%).

Cite Video arXiv Proceedings

(2024). Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation. In Proceedings of the Twelfth International Conference on Learning Representations (ICLR) (Acceptance Rate=31%).

Cite Code arXiv

Awards

Paper Awards

Outstanding Reviewer Awards

Other Awards

Scholarships

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

Workshop Organizer

Workshop Program Committee

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)

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