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Found 13 papers, 3 papers with code
Understanding the Limits of Poisoning Attacks in Episodic Reinforcement Learning
To understand the security threats to reinforcement learning (RL) algorithms, this paper studies poisoning attacks to manipulate \emph{any} order-optimal learning algorithm towards a targeted policy in episodic RL and examines the potential damage of two natural types of poisoning attacks, i. e., the manipulation of \emph{reward} and \emph{action}.
Saving Stochastic Bandits from Poisoning Attacks via Limited Data Verification
In particular, for the case of unlimited verifications, we show that with $O(\log T)$ expected number of verifications, a simple modified version of the ETC type bandit algorithm can restore the order optimal $O(\log T)$ regret irrespective of the amount of contamination used by the attacker.
Sequential Choice Bandits with Feedback for Personalizing users' experience
We then propose bandit algorithms for the two feedback models and show that upper and lower bounds on the regret are of the order of $\tilde{O}(N^{2/3})$ and $\tilde\Omega(N^{2/3})$, respectively, where $N$ is the total number of users.
Control Barriers in Bayesian Learning of System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints.
Learning-based attacks in Cyber-Physical Systems: Exploration, Detection, and Control Cost trade-offs
We study the trade-offs between the information acquired by the attacker from observations, the detection capabilities of the controller, and the control cost.
Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
This paper focuses on learning a model of system dynamics online while satisfying safety constraints. Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation. Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics.
Associative Convolutional Layers
We introduce a simple auxiliary neural network which can generate the convolutional filters of any CNN architecture from a low dimensional latent space.
Improved Intolerance Intervals and Size Bounds for a Schelling-Type Spin System
Finally, we show that when particles are placed on the infinite lattice $\mathbb{Z}^2$ rather than on a flat torus, for the values of $\tau$ mentioned above, sufficiently large $N$, and after a sufficiently long evolution time, any particle is contained in a large monochromatic region of size exponential in $N$, almost surely.
Social and Information Networks Mathematical Physics Mathematical Physics
Online learning with feedback graphs and switching costs
For the two special cases of symmetric PI setting and MAB, the expected regret of both of these algorithms is order optimal in the duration of the learning process.
Learning-based attacks in cyber-physical systems
In the case of scalar plants, we derive an upper bound on the attacker's deception probability for any measurable control policy when the attacker uses an arbitrary learning algorithm to estimate the system dynamics.
Distributed Chernoff Test: Optimal decision systems over networks
In the first case, the network nodes interact with each other through a central entity, which plays the role of a fusion center.
Stabilizing a linear system using phone calls: when time is information
Each symbol transmitted from a sensor to a controller in a closed-loop system is received subject to some to random delay.
Evolution and Steady State of a Long-Range Two-Dimensional Schelling-Type Spin System
When particles are placed on the infinite lattice $\mathbb{Z}^2$ rather than on a flat torus, for the values of $\tau$ mentioned above, sufficiently large $N$, and after a sufficiently long evolution time, any particle is contained in a large monochromatic region of size exponential in $N$, almost surely.
Probability Distributed, Parallel, and Cluster Computing Social and Information Networks Mathematical Physics Mathematical Physics
