2021

A Real-World Implementation of Unbiased Lift-based Bidding System
Daisuke Moriwaki, Yuta Hayakawa, Isshu Munemasa, Yuta Saito, Akira Matsui, Masashi Shibata
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Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. …
Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita
Cite Code Dataset Proceedings arXiv
Efficient Hyperparameter Optimization under Multi-Source Covariate Shift
A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same …
Masahiro Nomura, Yuta Saito
Cite Code Proceedings arXiv
Evaluating the Robustness of Off-Policy Evaluation
Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only …
Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, Kei Tateno
Cite Code Slides Proceedings arXiv
Optimal Off-Policy Evaluation from Multiple Logging Policies
We study off-policy evaluation (OPE) from multiple logging policies, each generating a dataset of fixed size, i.e., stratified …
Nathan Kallus, Yuta Saito, Masatoshi Uehara
Cite Code Proceedings arXiv