Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Statistical Efficiency of Thompson Sampling for Combinatorial
  Semi-Bandits   We investigate stochastic combinatorial multi-armed bandit with semi-bandit
feedback (CMAB). In CMAB, the question of the existence of an efficient policy
with an optimal asymptotic regret (up to a factor poly-logarithmic with the
action size) is still open for many families of distributions, including
mutually independent outcomes, and more generally the multivariate sub-Gaussian
family. We propose to answer the above question for these two families by
analyzing variants of the Combinatorial Thompson Sampling policy (CTS). For
mutually independent outcomes in $[0,1]$, we propose a tight analysis of CTS
using Beta priors. We then look at the more general setting of multivariate
sub-Gaussian outcomes and propose a tight analysis of CTS using Gaussian
priors. This last result gives us an alternative to the Efficient Sampling for
Combinatorial Bandit policy (ESCB), which, although optimal, is not
computationally efficient.

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The categories of this paper are: hep-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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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: ao-sci
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The categories of this paper are: math
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg