Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Why Non-myopic Bayesian Optimization is Promising and How Far Should We
  Look-ahead? A Study via Rollout   Lookahead, also known as non-myopic, Bayesian optimization (BO) aims to find
optimal sampling policies through solving a dynamic programming (DP)
formulation that maximizes a long-term reward over a rolling horizon. Though
promising, lookahead BO faces the risk of error propagation through its
increased dependence on a possibly mis-specified model. In this work we focus
on the rollout approximation for solving the intractable DP. We first prove the
improving nature of rollout in tackling lookahead BO and provide a sufficient
condition for the used heuristic to be rollout improving. We then provide both
a theoretical and practical guideline to decide on the rolling horizon
stagewise. This guideline is built on quantifying the negative effect of a
mis-specified model. To illustrate our idea, we provide case studies on both
single and multi-information source BO. Empirical results show the advantageous
properties of our method over several myopic and non-myopic BO algorithms.

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The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
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The categories of this paper are: nlin
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an