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 …

The problem of piecewise affine (PWA) regression and planning is of foundational
importance to the study of online learning, control, and robotics, where it provides a …

Bilinear dynamical systems are ubiquitous in many different domains and they can also be
used to approximate more general control-affine systems. This motivates the problem of …

Smoothed online learning has emerged as a popular framework to mitigate the substantial
loss in statistical and computational complexity that arises when one moves from classical to …

Inspired by competitive policy designs approaches in online learning, new control
paradigms such as competitive-ratio and regret-optimal control have been recently …

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

In this paper, we investigate the problem of system identification for autonomous Markov
jump linear systems (MJS) with complete state observations. We propose switched least …

AutoRegressive eXogenous (ARX) models form one of the most important model classes in
control theory, econometrics, and statistics, but they are yet to be understood in terms of their …

For the identification of switched systems with measured states and a measured switching
signal, this letter aims to analyze the effect of switching strategies on the estimation error …