Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Practical Scheme and Fast Algorithm to Tune the Lasso With Optimality
  Guarantees   We introduce a novel scheme for choosing the regularization parameter in
high-dimensional linear regression with Lasso. This scheme, inspired by
Lepski's method for bandwidth selection in non-parametric regression, is
equipped with both optimal finite-sample guarantees and a fast algorithm. In
particular, for any design matrix such that the Lasso has low sup-norm error
under an "oracle choice" of the regularization parameter, we show that our
method matches the oracle performance up to a small constant factor, and show
that it can be implemented by performing simple tests along a single Lasso
path. By applying the Lasso to simulated and real data, we find that our novel
scheme can be faster and more accurate than standard schemes such as
Cross-Validation.

36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph