Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimal Best-arm Identification in Linear Bandits   We study the problem of best-arm identification with fixed confidence in
stochastic linear bandits. The objective is to identify the best arm with a
given level of certainty while minimizing the sampling budget. We devise a
simple algorithm whose sampling complexity matches known instance-specific
lower bounds, asymptotically almost surely and in expectation. The algorithm
relies on an arm sampling rule that tracks an optimal proportion of arm draws,
and that remarkably can be updated as rarely as we wish, without compromising
its theoretical guarantees. Moreover, unlike existing best-arm identification
strategies, our algorithm uses a stopping rule that does not depend on the
number of arms. Experimental results suggest that our algorithm significantly
outperforms existing algorithms. The paper further provides a first analysis of
the best-arm identification problem in linear bandits with a continuous set of
arms.

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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: chem-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: econ
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: gr-qc