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 …

We study the problem of system identification and adaptive control in partially observable
linear dynamical systems. Adaptive and closed-loop system identification is a challenging …

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 …

In this paper, we consider the task of designing a Kalman Filter (KF) for an unknown and
partially observed autonomous linear time invariant system driven by process and sensor …

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 …

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 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 …

Learning how to effectively control unknown dynamical systems is crucial for intelligent
autonomous systems. This task becomes a significant challenge when the underlying …

In this article, we present a novel approach to reconstruct the topology of networked linear
dynamical systems with latent nodes. The network is allowed to have directed loops and bi …