Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Knowledge Gradient for Selection with Covariates: Consistency and
  Computation   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.

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The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
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The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
1
The categories of this paper are: cs
9
The categories of this paper are: nlin