Corpus ID: 236772651

# Debiasing Samples from Online Learning Using Bootstrap

@article{Chen2021DebiasingSF,
title={Debiasing Samples from Online Learning Using Bootstrap},
author={Ningyuan Chen and Xuefeng Gao and Yi Xiong},
journal={ArXiv},
year={2021},
volume={abs/2108.00236}
}
• Ningyuan Chen, Xuefeng Gao, Yi Xiong
• Published 2021
• Computer Science, Mathematics
• ArXiv
It has been recently shown in the literature [30, 35, 36] that the sample averages from online learning experiments are biased when used to estimate the mean reward. To correct the bias, off-policy evaluation methods, including importance sampling and doubly robust estimators, typically calculate the propensity score, which is unavailable in this setting due to unknown reward distribution and the adaptive policy. This paper provides a procedure to debias the samples using bootstrap, which doesn… Expand

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