38 citations · 109 across the 14 of their papers we have counts for
5 papers · 1 filter
Counterfactual Learning with General Data-generating Policies
Yusuke Narita, Kyohei Okumura, Akihiro Shimizu +1
Off-policy evaluation (OPE) attempts to predict the performance of counterfactual policies using log data from a different policy. We extend its applicability by developing an OPE…
Policy-Adaptive Estimator Selection for Off-Policy Evaluation
Takuma Udagawa, Haruka Kiyohara, Yusuke Narita +2
Off-policy evaluation (OPE) aims to accurately evaluate the performance of counterfactual policies using only offline logged data. Although many estimators have been developed, the…
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation
Yuta Saito, Shunsuke Aihara, Megumi Matsutani +1
Off-policy evaluation (OPE) aims to estimate the performance of hypothetical policies using data generated by a different policy. Because of its huge potential impact in practice,…
Debiased Off-Policy Evaluation for Recommendation Systems
Yusuke Narita, Shota Yasui, Kohei Yata
Efficient methods to evaluate new algorithms are critical for improving interactive bandit and reinforcement learning systems such as recommendation systems. A/B tests are reliable…
Efficient Counterfactual Learning from Bandit Feedback
Yusuke Narita, Shota Yasui, Kohei Yata
What is the most statistically efficient way to do off-policy evaluation and optimization with batch data from bandit feedback? For log data generated by contextual bandit algorith…