Distributed online optimization and online games have been increasingly researched in the
last decade, mostly motivated by their wide applications in sensor networks, robotics (eg …

This text presents an introduction to an emerging paradigm in control of dynamical systems
and differentiable reinforcement learning called online nonstochastic control. The new …

We study the problem of Online Convex Optimization (OCO) with memory, which allows loss
functions to depend on past decisions and thus captures temporal effects of learning …

We study predictive control in a setting where the dynamics are time-varying and linear, and
the costs are time-varying and well-conditioned. At each time step, the controller receives …

In the chasing convex bodies problem, an online player receives a request sequence of N
convex sets K 1,…, KN contained in a normed space X of dimension d. The player starts at x …

Abstract We study Model Predictive Control (MPC) and propose a general analysis pipeline
to bound its dynamic regret. The pipeline first requires deriving a perturbation bound for a …

Contextual Bayesian optimization (CBO) is a powerful framework for sequential decision-
making given side information, with important applications, eg, in wind energy systems. In …

In this article, we consider estimation and control in linear dynamical systems from the
perspective of regret minimization. Unlike most prior work in this area, we focus on the …

This paper presents competitive algorithms for a novel class of online optimization problems
with memory. We consider a setting where the learner seeks to minimize the sum of a hitting …

We study the problem of chasing convex bodies online: given a sequence of convex bodies
the algorithm must respond with points in an online fashion (ie, is chosen before is …