I am a fourth-year undergraduate student at Tokyo Institute of Technology. I am fortunate to be advised by Prof. Yusuke Narita (Yale University) and Prof. Nathan Kallus (Cornell Univeristy) during my undergrad study.
My research lies at the intersection of machine learning and causal inference called counterfactual machine learning. I am interested in the counterfactual nature of logged bandit feedback obtained from interactive systems, and ways of using biased real-world datasets to assist better decision making. Most recently, I have been focusing on bridging the gap between theory and practice in counterfactual machine learning.
- Counterfactual Machine Learning
- Off-Policy Evaluation (Bandit / Reinforcement Learning)
- Debiasing method for recommender systems
- Unbiased Learning-to-Rank
- Causal Inference
- Information Retrieval
- Statistical Learning Theory
April 2016 - Present
- Department of Industrial Engineering and Economics, Tokyo Institute of Technology
- Research Interests: Counterfactual Learning, Causal Inference, Recommender Systems
- 2020.08: We release Open Bandit Dataset (a large-scale dataset for bandit algorithms) and Open Bandit Pipeline (python package for bandit algorithms and off-policy evaluation).
- 2020.07: My solo paper: “Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions” has been accepted by RecSys2020.
- 2020.06: Our paper: “Unbiased Lift-based Bidding System” has been accepted by AdKDD2020.
- 2020.06: My solo paper: “Unbiased Pairwise Learning from Biased Implicit Feedback” has been accepted by ICTIR2020.
- 2020.06: Our paper: “Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models” has been accepted by ICML2020.
- 2020.04: My solo paper: “Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback” has been accepted by SIGIR2020.
- 2019.10: Our paper “Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback” has been accepted by WSDM2020.