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• W2963482016 abstract "We propose a new framework for designing estimators for off-policy evaluation in contextual bandits. Our approach is based on the asymptotically optimal doubly robust estimator, but we shrink the importance weights to minimize a bound on the mean squared error, which results in a better bias-variance tradeoff in finite samples. We use this optimization-based framework to obtain three estimators: (a) a weight-clipping estimator, (b) a new weight-shrinkage estimator, and (c) the first shrinkage-based estimator for combinatorial action sets. Extensive experiments in both standard and combinatorial bandit benchmark problems show that our estimators are highly adaptive and typically outperform state-of-the-art methods." @default.
• W2963482016 created "2019-07-30" @default.
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• W2963482016 date "2019-07-22" @default.
• W2963482016 modified "2023-09-27" @default.
• W2963482016 title "Doubly robust off-policy evaluation with shrinkage" @default.
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