Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Non-stationary Bandits and Meta-Learning with a Small Set of Optimal
  Arms   We study a sequential decision problem where the learner faces a sequence of
$K$-armed stochastic bandit tasks. The tasks may be designed by an adversary,
but the adversary is constrained to choose the optimal arm of each task in a
smaller (but unknown) subset of $M$ arms. The task boundaries might be known
(the bandit meta-learning setting), or unknown (the non-stationary bandit
setting), and the number of tasks $N$ as well as the total number of rounds $T$
are known ($N$ could be unknown in the meta-learning setting). We design an
algorithm based on a reduction to bandit submodular maximization, and show that
its regret in both settings is smaller than the simple baseline of
$\tilde{O}(\sqrt{KNT})$ that can be obtained by using standard algorithms
designed for non-stationary bandit problems. For the bandit meta-learning
problem with fixed task length $\tau$, we show that the regret of the algorithm
is bounded as $\tilde{O}(N\sqrt{M \tau}+N^{2/3})$. Under additional assumptions
on the identifiability of the optimal arms in each task, we show a bandit
meta-learning algorithm with an improved $\tilde{O}(N\sqrt{M \tau}+N^{1/2})$
regret.

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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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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: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ph
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: physics
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The categories of this paper are: astro-ph