Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Meta-Thompson Sampling   Efficient exploration in bandits is a fundamental online learning problem. We
propose a variant of Thompson sampling that learns to explore better as it
interacts with bandit instances drawn from an unknown prior. The algorithm
meta-learns the prior and thus we call it MetaTS. We propose several efficient
implementations of MetaTS and analyze it in Gaussian bandits. Our analysis
shows the benefit of meta-learning and is of a broader interest, because we
derive a novel prior-dependent Bayes regret bound for Thompson sampling. Our
theory is complemented by empirical evaluation, which shows that MetaTS quickly
adapts to the unknown prior.

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The categories of this paper are: cond-mat
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: solv-int