About Me

I am a fourth year undergraduate at Tokyo Institute of Technology.

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 the intersection of counterfactual machine learning and information retrieval.

Research Interests

  • Counterfactual Machine Learning
    • Offline evaluation of bandit policies
    • De-biasing recommender systems
    • Unbiased Learning-to-Rank
  • Causal Inference
  • Information Retrieval
  • Unsupervised Domain Adaptation


  • 2019.11: My paper: “Unsupervised Domain Adaptation Meets Offline Recommender Learning” has been accepted to NewInML session (co-located with NeurIPS’19)
  • 2019.11: I started working at ZOZO Technologies, Inc. and Jinch Co., Ltd., as a Research Internshop.
  • 2019.10: Our paper “Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback” has been accepted to WSDM’20
  • 2019.10: Our papers “Dual Learning Algorithm for Delayed Feedback in Display Advertising” and “Unbiased Pairwise Learning from Implicit Feedback” have been accepted to CausalML Workshop at NeurIPS’19.
  • 2019.08: Our paper “Counterfactual Cross Validation” has been accepted to REVEAL Workshop at RecSys’19.
  • 2019.06: I started working at CyberAgent, Inc., AI Lab as a Research Internshop.
  • 2018.12: Our paper “Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data” has been accepted to SDM’19.