Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Statistical Cost of Robust Kernel Hyperparameter Tuning   This paper studies the statistical complexity of kernel hyperparameter tuning
in the setting of active regression under adversarial noise. We consider the
problem of finding the best interpolant from a class of kernels with unknown
hyperparameters, assuming only that the noise is square-integrable. We provide
finite-sample guarantees for the problem, characterizing how increasing the
complexity of the kernel class increases the complexity of learning kernel
hyperparameters. For common kernel classes (e.g. squared-exponential kernels
with unknown lengthscale), our results show that hyperparameter optimization
increases sample complexity by just a logarithmic factor, in comparison to the
setting where optimal parameters are known in advance. Our result is based on a
subsampling guarantee for linear regression under multiple design matrices,
combined with an {\epsilon}-net argument for discretizing kernel
parameterizations.

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The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: chem-ph
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The categories of this paper are: q-alg