Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Support Recovery for the Drift Coefficient of High-Dimensional
  Diffusions   Consider the problem of learning the drift coefficient of a $p$-dimensional
stochastic differential equation from a sample path of length $T$. We assume
that the drift is parametrized by a high-dimensional vector, and study the
support recovery problem when both $p$ and $T$ can tend to infinity. In
particular, we prove a general lower bound on the sample-complexity $T$ by
using a characterization of mutual information as a time integral of
conditional variance, due to Kadota, Zakai, and Ziv. For linear stochastic
differential equations, the drift coefficient is parametrized by a $p\times p$
matrix which describes which degrees of freedom interact under the dynamics. In
this case, we analyze a $\ell_1$-regularized least squares estimator and prove
an upper bound on $T$ that nearly matches the lower bound on specific classes
of sparse matrices.

35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga