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.
- 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.