Others
Involved Research Projects
Open Bandit Project
Open Bandit Dataset is a public real-world logged bandit feedback data. The dataset is provided by ZOZO, Inc., the largest Japanese fashion e-commerce company. It is especially suitable for off-policy evaluation (OPE), which attempts to predict the performance of hypothetical policies using data generated by a different, past policy. The data contain the ground-truth about the performance of several bandit policies and enable the fair comparisons of different OPE estimators.
Open Bandit Pipeline is a series of implementations of dataset preprocessing, offline bandit simulation, and evaluation of OPE estimators. This pipeline allows researchers to focus on building their OPE estimator and easily compare it with others’ methods in realistic and reproducible ways. Thus, it facilitates reproducible research on bandit algorithms and off-policy evaluation.
[Paper] [Open Bandit Pipeline] [Docs] [Open Bandit Dataset]
Scholarships and Awards
- Funai Overseas Scholarship Doctoral research fellowship by the Funai Foundation (a private foundation in Japan). Granted two full years of tuition plus a monthly stipend of $3,000 for living expenses.
- NeurIPS 2021 Outstanding Reviewer Award
- The SIGIR 2020 Student Travel Grant Program
Professional Service
Conference Program Committee
Workshop Program Committee
- NeurIPS 2021 Workshop on Offline Reinforcement Learning
- NeurIPS 2021 Workshop on Causal Inference Challenges in Sequential Decision Making
Journal Reviewer
- IEEE Transactions on Knowledge and Data Engineering (TKDE)
Conference Oral Presentations
“A Large-scale Open Dataset for Bandit Algorithms”
RecSys 2020 Workshop on Bandit and Reinforcement Learning from User Interactions (REVEAL2020)“Doubly Robust Estimator for Ranking Metrics with Post-Click Conversions”
ACM Conference on Recommender Systems (RecSys2020)“Unbiased Pairwise Learning from Biased Implicit Feedback’’
International Conference on the Theory of Information Retrieval (ICTIR2020)“Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback”
International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2020)“A Large-scale Open Dataset for Bandit Algorithms”
ICML 2020 Workshop on Real World Experiment Design and Active Learning (RealML2020)“Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback’’
International Conference on Web Search and Data Mining (WSDM20)“Unbiased Pairwise Learning from Implicit Feedback’’
NeurIPS 2019 Workshop on Causal Machine Learning (CausalML)“Doubly Robust Prediction and Evaluation Methods Improve Uplift Modeling for Observational Data’’
SIAM Conference on Data Mining (SDM2019)