Publications de Andrea Martin | EPFL Graph Search

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Recent years have witnessed a growing integration of automatic control into a wide range of cyber-physical systems, including electrical grids and autonomous transportation networks. These systems operate in uncertain and dynamic environments, exhibit comp ...

Toward bridging classical optimal control and online learning, regret minimization has recently been proposed as a control design criterion. This competitive paradigm penalizes the loss relative to the optimal control actions chosen by a clairvoyant policy ...

As we transition toward the deployment of data-driven controllers for black-box cyberphysical systems, complying with hard safety constraints becomes a primary concern. Two key aspects should be addressed when input-output data are corrupted by noise: how ...

We study control of constrained linear systems with only partial statistical information about the uncertainty affecting the system dynamics and the sensor measurements. Specifically, given a finite collection of disturbance realizations drawn from a gener ...

We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future d ...

Recent work in data-driven control has revived behavioral theory to perform a variety of complex control tasks, by directly plugging libraries of past input-output trajectories into optimal control problems. Despite recent advances, a key aspect remains un ...

The increasing reliance on numerical methods for controlling dynamical systems and training machine learning models underscores the need to devise algorithms that dependably and efficiently navigate complex optimization landscapes. Classical gradient desce ...

We consider control of uncertain linear time-varying stochastic systems from the perspective of regret minimization. Specifically, we focus on the problem of designing a feedback controller that minimizes the loss relative to a clairvoyant optimal policy t ...

How can we synthesize a safe and near-optimal control policy for a partially-observed system, if all we are given is one historical input/output trajectory that has been corrupted by noise? To address this challenge, we suggest a novel data-driven controll ...

Andrea Martin

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Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

On the Guarantees of Minimizing Regret in Receding Horizon

Near-Optimal Design of Safe Output-Feedback Controllers From Noisy Data

Distributionally robust infinite-horizon control: from a pool of samples to the design of dependable controllers

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

A Behavioral Input-Output Parametrization of Control Policies with Suboptimality Guarantees

Learning to Optimize with Convergence Guarantees Using Nonlinear System Theory

Regret optimal control for uncertain stochastic systems

Learning Robustly Safe Output Feedback Controllers from Noisy Data with Performance Guarantees