Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient and passive learning of networked dynamical systems driven by
  non-white exogenous inputs   We consider a networked linear dynamical system with $p$ agents/nodes. We
study the problem of learning the underlying graph of interactions/dependencies
from observations of the nodal trajectories over a time-interval $T$. We
present a regularized non-casual consistent estimator for this problem and
analyze its sample complexity over two regimes: (a) where the interval $T$
consists of $n$ i.i.d. observation windows of length $T/n$ (restart and
record), and (b) where $T$ is one continuous observation window (consecutive).
Using the theory of $M$-estimators, we show that the estimator recovers the
underlying interactions, in either regime, in a time-interval that is
logarithmic in the system size $p$. To the best of our knowledge, this is the
first work to analyze the sample complexity of learning linear dynamical
systems \emph{driven by unobserved not-white wide-sense stationary (WSS)
inputs}.

18
The categories of this paper are: econ
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph