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• W2952979408 abstract "Knowledge gradient is a design principle for developing Bayesian sequential sampling policies to solve optimization problems. In this paper we consider the ranking and selection problem in the presence of covariates, where the best alternative is not universal but depends on the covariates. In this context, we prove that under minimal assumptions, the sampling policy based on knowledge gradient is consistent, in the sense that following the policy the best alternative as a function of the covariates will be identified almost surely as the number of samples grows. We also propose a stochastic gradient ascent algorithm for computing the sampling policy and demonstrate its performance via numerical experiments." @default.
• W2952979408 created "2019-06-27" @default.
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• W2952979408 date "2019-06-12" @default.
• W2952979408 modified "2023-10-16" @default.
• W2952979408 title "Knowledge Gradient for Selection with Covariates: Consistency and Computation" @default.
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• W2952979408 doi "https://doi.org/10.48550/arxiv.1906.05098" @default.
• W2952979408 hasPublicationYear "2019" @default.
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