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Found 8 papers, 0 papers with code
Model-based and Data-based Dynamic Output Feedback for Externally Positive Systems
We leverage the static form of the controller to derive output-feedback controllers that achieve monotonic output tracking of a constant non-negative reference output.
Imitation and Transfer Learning for LQG Control
In this paper we study an imitation and transfer learning setting for Linear Quadratic Gaussian (LQG) control, where (i) the system dynamics, noise statistics and cost function are unknown and expert data is provided (that is, sequences of optimal inputs and outputs) to learn the LQG controller, and (ii) multiple control tasks are performed for the same system but with different LQG costs.
Behavioral Feedback for Optimal LQG Control
In this work, we revisit the Linear Quadratic Gaussian (LQG) optimal control problem from a behavioral perspective.
Robust Adversarial Classification via Abstaining
We propose metrics to quantify the nominal performance of a classifier with an abstain option and its robustness against adversarial perturbations.
Learning Lipschitz Feedback Policies from Expert Demonstrations: Closed-Loop Guarantees, Generalization and Robustness
In this work, we propose a framework to learn feedback control policies with guarantees on closed-loop generalization and adversarial robustness.
Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing
In contrast to regularization-based approaches, we formulate the adversarially robust learning problem as one of loss minimization with a Lipschitz constraint, and show that the saddle point of the associated Lagrangian is characterized by a Poisson equation with weighted Laplace operator.
On the Robustness of Data-Driven Controllers for Linear Systems
This paper proposes a new framework and several results to quantify the performance of data-driven state-feedback controllers for linear systems against targeted perturbations of the training data.
A Fundamental Performance Limitation for Adversarial Classification
Despite the widespread use of machine learning algorithms to solve problems of technological, economic, and social relevance, provable guarantees on the performance of these data-driven algorithms are critically lacking, especially when the data originates from unreliable sources and is transmitted over unprotected and easily accessible channels.
