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

In-context learning (ICL) is a type of prompting where a transformer model operates on a
sequence of (input, output) examples and performs inference on-the-fly. In this work, we …

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 …

Modern language models rely on the transformer architecture and attention mechanism to
perform language understanding and text generation. In this work, we study learning a 1 …

Physics-informed learning for industrial Internet is essential especially to safety issues.
Consequently, various methods have been developed to conduct Industrial Internet of …

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 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 …

In this paper, we derive a PAC-Bayes bound on the generalisation gap, in a supervised time-
series setting for a special class of discrete-time non-linear dynamical systems. This class …

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 …

In this paper we provide direct data-driven expressions for the Linear Quadratic Regulator
(LQR), the Kalman filter, and the Linear Quadratic Gaussian (LQG) controller using a finite …