Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Thompson Sampling with a Mixture Prior   We study Thompson sampling (TS) in online decision making, where the
uncertain environment is sampled from a mixture distribution. This is relevant
in multi-task learning, where a learning agent faces different classes of
problems. We incorporate this structure in a natural way by initializing TS
with a mixture prior, and call the resulting algorithm MixTS. To analyze MixTS,
we develop a novel and general proof technique for analyzing the concentration
of mixture distributions. We use it to prove Bayes regret bounds for MixTS in
both linear bandits and finite-horizon reinforcement learning. Our bounds
capture the structure of the prior, depend on the number of mixture components
and their widths. We also demonstrate the empirical effectiveness of MixTS in
synthetic and real-world experiments.

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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat