Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hedging parameter selection for basis pursuit   In Compressed Sensing and high dimensional estimation, signal recovery often
relies on sparsity assumptions and estimation is performed via
$\ell_1$-penalized least-squares optimization, a.k.a. LASSO. The $\ell_1$
penalisation is usually controlled by a weight, also called "relaxation
parameter", denoted by $\lambda$. It is commonly thought that the practical
efficiency of the LASSO for prediction crucially relies on accurate selection
of $\lambda$. In this short note, we propose to consider the hyper-parameter
selection problem from a new perspective which combines the Hedge online
learning method by Freund and Shapire, with the stochastic Frank-Wolfe method
for the LASSO. Using the Hedge algorithm, we show that a our simple selection
rule can achieve prediction results comparable to Cross Validation at a
potentially much lower computational cost.

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The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
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The categories of this paper are: cs
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph