Mathematical optimization is one of the cornerstones of modern engineering research and
practice. Yet, throughout all application domains, mathematical optimization is, for the most …

Renewable energy integration is an effective measure to resolve environmental problems
and implement sustainable development, yet the volatility of wind and solar generation has …

We study the impact of predictions in online Linear Quadratic Regulator control with both
stochastic and adversarial disturbances in the dynamics. In both settings, we characterize …

We examine the problem of designing learning-augmented algorithms for metrical task
systems (MTS) that exploit machine-learned advice while maintaining rigorous, worst-case …

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 …

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 …

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 a general form of Smoothed Online Convex Optimization, aka SOCO, including
multi-step switching costs and feedback delay. We propose a novel machine learning (ML) …

We consider control in linear time-varying dynamical systems from the perspective of regret
minimization. Unlike most prior work in this area, we focus on the problem of designing an …