Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

This tutorial serves as an introduction to recently developed non-asymptotic methods in the
theory of-mainly linear-system identification. We emphasize tools we deem particularly …

In this paper, we investigate when system identification is statistically easy or hard, in the
finite sample regime. Statistically easy to learn linear system classes have sample …

Linear dynamical systems are the foundational statistical model upon which control theory is
built. Both the celebrated Kalman filter and the linear quadratic regulator require knowledge …

In this paper, we study the statistical difficulty of learning to control linear systems. We focus
on two standard benchmarks, the sample complexity of stabilization, and the regret of the …

In this article, we consider the problem of predicting observations generated online by an
unknown, partially observable linear system, which is driven by Gaussian noise. In the linear …

We consider the problem of estimating a linear time-invariant (LTI) dynamical system from a
single trajectory via streaming algorithms, which is encountered in several applications …

This paper is concerned with the fundamental limit on the identification of discrete-time SISO
(Single Input Single Output) system, where the diagonal canonical form of the system is …

Learning a dynamical system from input/output data is a fundamental task in the control
design pipeline. In the partially observed setting there are two components to identification …

We consider the problem of learning a realization of a partially observed dynamical system
with linear state transitions and bilinear observations. Under very mild assumptions on the …