Deep learning is a topic of considerable current interest. The availability of massive data
collections and powerful software resources has led to an impressive amount of results in …

The commonly adopted route to control a dynamic system and make it follow the desired
behavior consists of two steps. First, a model of the system is learned from input–output data …

We study the sample complexity of the Sign-Perturbed Sums (SPS) method, which
constructs exact, non-asymptotic confidence regions for the true system parameters under …

Classifiers based on neural networks (nn) often lack a measure of uncertainty in the
predicted class. We propose a method to estimate the probability mass function (pmf) of the …

Complementing data-driven models of dynamic systems with certificates of reliability and
safety is of critical importance in many applications, such as in the design of robust control …

The identification accuracy of systems relies on sufficiently-rich measurement information.
Collecting large numbers of informative data is costly and burdensome due to various …

Abstract Sign-Perturbed Sum (SPS) is a powerful finite-sample system identification
algorithm which can construct confidence regions for the true data generating system with …

This paper investigates the existence of a separation principle between model identification
and control design in the context of model predictive control. First, we elucidate that the …

Sign-Perturbed Sums (SPS) is a system identification method that constructs confidence
regions for the unknown system parameters. In this paper, we study SPS for ARX systems …

We study non-parametric frequency-domain system identification from a finite-sample
perspective. We assume an open loop scenario where the excitation input is periodic and …