Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Unified Dynamic Approach to Sparse Model Selection   Sparse model selection is ubiquitous from linear regression to graphical
models where regularization paths, as a family of estimators upon the
regularization parameter varying, are computed when the regularization
parameter is unknown or decided data-adaptively. Traditional computational
methods rely on solving a set of optimization problems where the regularization
parameters are fixed on a grid that might be inefficient. In this paper, we
introduce a simple iterative regularization path, which follows the dynamics of
a sparse Mirror Descent algorithm or a generalization of Linearized Bregman
Iterations with nonlinear loss. Its performance is competitive to
\texttt{glmnet} with a further bias reduction. A path consistency theory is
presented that under the Restricted Strong Convexity (RSC) and the
Irrepresentable Condition (IRR), the path will first evolve in a subspace with
no false positives and reach an estimator that is sign-consistent or of minimax
optimal $\ell_2$ error rate. Early stopping regularization is required to
prevent overfitting. Application examples are given in sparse logistic
regression and Ising models for NIPS coauthorship.

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The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph