Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bandits with Knapsacks beyond the Worst-Case   Bandits with Knapsacks (BwK) is a general model for multi-armed bandits under
supply/budget constraints. While worst-case regret bounds for BwK are
well-understood, we present three results that go beyond the worst-case
perspective. First, we provide upper and lower bounds which amount to a full
characterization for logarithmic, instance-dependent regret rates. Second, we
consider "simple regret" in BwK, which tracks algorithm's performance in a
given round, and prove that it is small in all but a few rounds. Third, we
provide a general "reduction" from BwK to bandits which takes advantage of some
known helpful structure, and apply this reduction to combinatorial
semi-bandits, linear contextual bandits, and multinomial-logit bandits. Our
results build on the BwK algorithm from \citet{AgrawalDevanur-ec14}, providing
new analyses thereof.

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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
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The categories of this paper are: bayes-an
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The categories of this paper are: math
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: eess
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The categories of this paper are: q-fin