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Found 29 papers, 1 papers with code
Online Change Points Detection for Linear Dynamical Systems with Finite Sample Guarantees
We develop a data-dependent threshold that can be used in our test that allows one to achieve a pre-specified upper bound on the probability of making a false alarm.
Robust Online Covariance and Sparse Precision Estimation Under Arbitrary Data Corruption
We propose an online algorithm to estimate the sparse inverse covariance (i. e., precision) matrix despite this corruption.
Learning Linearized Models from Nonlinear Systems with Finite Data
Identifying a linear system model from data has wide applications in control theory.
Deep Reinforcement Learning Based Framework for Mobile Energy Disseminator Dispatching to Charge On-the-Road Electric Vehicles
In the first component of the framework, we develop a realistic reinforcement learning environment termed "ChargingEnv" which incorporates a reliable charging simulation system that accounts for common practical issues in wireless charging deployment, specifically, the charging panel misalignment.
On the Benefits of Leveraging Structural Information in Planning Over the Learned Model
Model-based Reinforcement Learning (RL) integrates learning and planning and has received increasing attention in recent years.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Learning Dynamical Systems by Leveraging Data from Similar Systems
We consider the problem of learning the dynamics of a linear system when one has access to data generated by an auxiliary system that shares similar (but not identical) dynamics, in addition to data from the true system.
Robust Online and Distributed Mean Estimation Under Adversarial Data Corruption
We study robust mean estimation in an online and distributed scenario in the presence of adversarial data attacks.
Finite Sample Guarantees for Distributed Online Parameter Estimation with Communication Costs
We provide non-asymptotic bounds on the estimation error, leveraging the statistical properties of the underlying model.
Optimal Mitigation of SIR Epidemics Under Model Uncertainty
Distinct from existing works on leveraging control strategies in epidemic spreading, we propose a testing strategy by overestimating the seriousness of the epidemic and study the feasibility of the system under the impact of model parameter uncertainty.
Resilience for Distributed Consensus with Constraints
By applying this new device to multi-agent systems, we introduce network and constraint redundancy conditions under which resilient constrained consensus can be achieved with an exponential convergence rate.
A Survey of Graph-Theoretic Approaches for Analyzing the Resilience of Networked Control Systems
As the scale of networked control systems increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks increase in importance.
Identifying the Dynamics of a System by Leveraging Data from Similar Systems
We study the problem of identifying the dynamics of a linear system when one has access to samples generated by a similar (but not identical) system, in addition to data from the true system.
Learning the Dynamics of Autonomous Linear Systems From Multiple Trajectories
Existing results on learning rate and consistency of autonomous linear system identification rely on observations of steady state behaviors from a single long trajectory, and are not applicable to unstable systems.
Peak Infection Time for a Networked SIR Epidemic with Opinion Dynamics
We propose an SIR epidemic model coupled with opinion dynamics to study an epidemic and opinions spreading in a network of communities.
Distributed Estimation of Sparse Inverse Covariances
Learning the relationships between various entities from time-series data is essential in many applications.
Parameter Estimation in Epidemic Spread Networks Using Limited Measurements
We study the problem of estimating the parameters (i. e., infection rate and recovery rate) governing the spread of epidemics in networks.
Towards Resilience for Multi-Agent $QD$-Learning
This paper considers the multi-agent reinforcement learning (MARL) problem for a networked (peer-to-peer) system in the presence of Byzantine agents.
The Effect of Behavioral Probability Weighting in a Simultaneous Multi-Target Attacker-Defender Game
Each node has a certain value to the attacker and the defender, along with a probability of being successfully compromised, which is a function of the investments in that node by both players.
On a Network SIS Epidemic Model with Cooperative and Antagonistic Opinion Dynamics
We propose a mathematical model to study coupled epidemic and opinion dynamics in a network of communities.
On the Computational Complexity of the Secure State-Reconstruction Problem
To better understand this, we show that when the $\mathbf{A}$ matrix of the linear system has unitary geometric multiplicity, the gap disappears, i. e., eigenvalue observability coincides with sparse observability, and there exists a polynomial time algorithm to reconstruct the state provided the state can be reconstructed.
Near-Optimal Data Source Selection for Bayesian Learning
We then show that the data source selection problem can be transformed into an instance of the submodular set covering problem studied in the literature, and provide a standard greedy algorithm to solve the data source selection problem with provable performance guarantees.
Morshed: Guiding Behavioral Decision-Makers towards Better Security Investment in Interdependent Systems
We model the behavioral biases of human decision-making in securing interdependent systems and show that such behavioral decision-making leads to a suboptimal pattern of resource allocation compared to non-behavioral (rational) decision-making.
BASCPS: How does behavioral decision making impact the security of cyber-physical systems?
We model the security investment decisions made by the defenders as a security game.
Cryptography and Security Computer Science and Game Theory
Distributed Hypothesis Testing and Social Learning in Finite Time with a Finite Amount of Communication
We consider the problem of distributed hypothesis testing (or social learning) where a network of agents seeks to identify the true state of the world from a finite set of hypotheses, based on a series of stochastic signals that each agent receives.
Distributed Inference with Sparse and Quantized Communication
We prove that our rule guarantees convergence to the true state exponentially fast almost surely despite sparse communication, and that it has the potential to significantly reduce information flow from uninformative agents to informative agents.
A Communication-Efficient Algorithm for Exponentially Fast Non-Bayesian Learning in Networks
We introduce a simple time-triggered protocol to achieve communication-efficient non-Bayesian learning over a network.
A New Approach to Distributed Hypothesis Testing and Non-Bayesian Learning: Improved Learning Rate and Byzantine-Resilience
We study a setting where a group of agents, each receiving partially informative private signals, seek to collaboratively learn the true underlying state of the world (from a finite set of hypotheses) that generates their joint observation profiles.
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
Under minimal requirements on the signal structures of the agents and the underlying communication graph, we establish consistency of the proposed belief update rule, i. e., we show that the actual beliefs of the agents asymptotically concentrate on the true state almost surely.
Distributed Optimization Under Adversarial Nodes
We then propose a resilient distributed optimization algorithm that guarantees that the non-adversarial nodes converge to the convex hull of the minimizers of their local functions under certain conditions on the graph topology, regardless of the actions of a certain number of adversarial nodes.
Systems and Control Distributed, Parallel, and Cluster Computing Optimization and Control
