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Found 8 papers, 1 papers with code
Transfer in Sequential Multi-armed Bandits via Reward Samples
We consider a sequential stochastic multi-armed bandit problem where the agent interacts with bandit over multiple episodes.
Approximate Stability Radius Analysis and Design in Linear Systems
The robustness of the stability properties of dynamical systems in the presence of unknown/adversarial perturbations to system parameters is a desirable property.
Minimum-norm Sparse Perturbations for Opacity in Linear Systems
In this paper, we propose algorithms to compute the minimum sparse perturbation to be added to a system to make its initial states opaque.
On Connections between Opacity and Security in Linear Systems
In this paper, we show that a fundamental trade-off exists between these properties for a linear dynamical system, in the sense that if an opaque system is subjected to attacks, all attacks cannot be detected.
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.
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.
