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- W3101657791 abstract "We apply empirical likelihood techniques to contextual bandit policy value estimation, confidence intervals, and learning. We propose a tighter estimator for off-policy evaluation with improved statistical performance over previous proposals. Coupled with this estimator is a confidence interval which also improves over previous proposals. We then harness these to improve learning from contextual bandit data. Each of these is empirically evaluated to show good performance against strong baselines in finite sample regimes." @default.
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- W3101657791 date "2020-09-01" @default.
- W3101657791 modified "2023-09-24" @default.
- W3101657791 title "Empirical Likelihood for Contextual Bandits" @default.
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