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Found 11 papers, 0 papers with code
Beyond PCA: A Probabilistic Gram-Schmidt Approach to Feature Extraction
Specifically, by applying the GS process over a family of functions which presumably captures the nonlinear dependencies in the data, we construct a series of covariance matrices that can either be used to identify new large-variance directions, or to remove those dependencies from the principal components.
Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights
We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN.
Model-Free Learning and Optimal Policy Design in Multi-Agent MDPs Under Probabilistic Agent Dropout
The controller's objective is to find an optimal policy that maximizes the value of the expected system given a priori knowledge of the agents' dropout probabilities.
Corrected: On Confident Policy Evaluation for Factored Markov Decision Processes with Node Dropouts
In this work we investigate an importance sampling approach for evaluating policies for a structurally time-varying factored Markov decision process (MDP), i. e. the policy's value is estimated with a high-probability confidence interval.
ECCO: Equivalent Circuit Controlled Optimization
To find the value of the critical point, we propose a time step search routine for Forward Euler discretization that controls the local truncation error, a method adapted from circuit simulation ideas.
Reducing Attack Opportunities Through Decentralized Event-Triggered Control
Decentralized control systems are widely used in a number of situations and applications.
Cluster-Based Control of Transition-Independent MDPs
To efficiently find a policy in this rapidly scaling space, we propose a clustered Bellman operator that optimizes over the action space for one cluster at any evaluation.
Ensuring Resilience Against Stealthy Attacks on Cyber-Physical Systems
To counter such attackers, a response scheme must be implemented that keeps the attacker from corrupting the inputs and outputs of the system for certain periods of time.
Stochastic Multi-armed Bandits with Non-stationary Rewards Generated by a Linear Dynamical System
The proposed strategy for this stochastic multi-armed bandit variant is to learn a model of the dynamical system while choosing the optimal action based on the learned model.
Exploring the consequences of cyber attacks on Powertrain Cyber Physical Systems
This paper proposes a novel approach for the study of cyber-attacks against the powertrain of a generic vehicle.
Toward Safe and Efficient Human-Robot Interaction via Behavior-Driven Danger Signaling
It is shown that based upon the danger awareness coefficient and the proposed learning method, the robot can build a predictive human model to anticipate the human's future actions.
Robotics Systems and Control Systems and Control
