Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online Learning with Low Rank Experts   We consider the problem of prediction with expert advice when the losses of
the experts have low-dimensional structure: they are restricted to an unknown
$d$-dimensional subspace. We devise algorithms with regret bounds that are
independent of the number of experts and depend only on the rank $d$. For the
stochastic model we show a tight bound of $\Theta(\sqrt{dT})$, and extend it to
a setting of an approximate $d$ subspace. For the adversarial model we show an
upper bound of $O(d\sqrt{T})$ and a lower bound of $\Omega(\sqrt{dT})$.

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The categories of this paper are: cond-mat
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: stat
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph
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The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th