Yuta Saito

Yuta Saito

Second-year CS Ph.D. Student

Cornell University

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

Recent & Upcoming Talks

Workshop Tutorial at CONSEQUENCES, RecSys2022
CONSEQUENCES Workshop RecSys2022 Tutorial on Large-Scale Off-Policy Evaluation
Counterfactual Tutorial at KDD2022
KDD2022 Tutorial on Counterfactual Evaluation and Learning
Counterfactual Tutorial at RecSys2021
RecSys2021 Tutorial on Counterfactual Evaluation and Learning

Recent Publications

Quickly discover relevant content by filtering publications.
(2022). Policy-Adaptive Estimator Selection for Off-Policy Evaluation. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (Acceptance rate=19.6%).

Cite Code arXiv

(2022). Counterfactual Evaluation and Learning for Interactive Systems. In Proceedings of the 28th SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD).

Cite Code Proceedings Website

(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)).

Cite Proceedings

(2022). Unbiased Recommender Learning from Biased Graded Implicit Feedback. WSDM 2022 Workshop on Decision Making for Modern Information Retrieval System.

Cite PDF

(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).

Cite

Awards

Paper Awards

Outstanding Reviewer Awards

Other Awards

Scholarships

Relevant Courses

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)

Contact