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Found 25 papers, 4 papers with code
Perspectives on Contractivity in Control, Optimization, and Learning
Contraction theory is a mathematical framework for studying the convergence, robustness, and modularity properties of dynamical systems and algorithms.
Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
Global stability and robustness guarantees in learned dynamical systems are essential to ensure well-behavedness of the systems in the face of uncertainty.
IDKM: Memory Efficient Neural Network Quantization via Implicit, Differentiable k-Means
We also introduce a variant, IDKM with Jacobian-Free-Backpropagation (IDKM-JFB), for which the time complexity of the gradient calculation is independent of $t$ as well.
Positive Competitive Networks for Sparse Reconstruction
Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without non-negativity constraints.
Multiplayer Homicidal Chauffeur Reach-Avoid Games: A Pursuit Enclosure Function Approach
We propose a cooperative strategy for the pursuers based on subgames for multiple pursuers against one evader and optimal task allocation.
Singular Perturbation via Contraction Theory
In this paper, we provide a novel contraction-theoretic approach to analyze two-time scale systems.
A Stochastic Surveillance Stackelberg Game: Co-Optimizing Defense Placement and Patrol Strategy
Stochastic patrol routing is known to be advantageous in adversarial settings; however, the optimal choice of stochastic routing strategy is dependent on a model of the adversary.
Learning Robust Data-based LQG Controllers from Noisy Data
In this work, a data-based formulation for computing the steady-state Kalman gain is proposed based on semi-definite programming (SDP) using some noise-free input-state-output data.
Convergence, Consensus and Dissensus in the Weighted-Median Opinion Dynamics
Moreover, we prove a necessary and sufficient graph-theoretic condition for the almost-sure convergence to consensus, as well as a sufficient graph-theoretic condition for almost-sure persistent dissensus.
Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach
Third, we use the upper bounds of the Lipschitz constants and the upper bounds of the tight inclusion functions to design two algorithms for the training and robustness verification of implicit neural networks.
Data-driven Self-triggered Control via Trajectory Prediction
Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity.
Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks
We use interval reachability analysis to obtain robustness guarantees for implicit neural networks (INNs).
Minimax Flow over Acyclic Networks: Distributed Algorithms and Microgrid Application
Given a flow network with variable suppliers and fixed consumers, the minimax flow problem consists in minimizing the maximum flow between nodes, subject to flow conservation and capacity constraints.
Modeling Human-AI Team Decision Making
AI and humans bring complementary skills to group deliberations.
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach
First, given an implicit neural network, we introduce a related embedded network and show that, given an $\ell_\infty$-norm box constraint on the input, the embedded network provides an $\ell_\infty$-norm box overapproximation for the output of the given network.
Data-Driven Resilient Predictive Control under Denial-of-Service
Finally, a numerical example is given to validate the effectiveness of the proposed control method.
Robust Implicit Networks via Non-Euclidean Contractions
Additionally, we design a training problem with the well-posedness condition and the average iteration as constraints and, to achieve robust models, with the input-output Lipschitz constant as a regularizer.
A Contraction Theory Approach to Optimization Algorithms from Acceleration Flows
Much recent interest has focused on the design of optimization algorithms from the discretization of an associated optimization flow, i. e., a system of differential equations (ODEs) whose trajectories solve an associated optimization problem.
Resilient Control under Quantization and Denial-of-Service: Co-designing a Deadbeat Controller and Transmission Protocol
When both input and output channels are subject to DoS attacks and quantization, the proposed structure is shown able to decouple the encoding schemes for input, output, and estimated output signals.
Combining Physics and Machine Learning for Network Flow Estimation
The flow estimation problem consists of predicting missing edge flows in a network (e. g., traffic, power and water) based on partial observations.
Distributed Wasserstein Barycenters via Displacement Interpolation
Consider a multi-agent system whereby each agent has an initial probability measure.
Expertise and confidence explain how social influence evolves along intellective tasks
Lastly, we propose a novel approach using deep neural networks on a pre-trained text embedding model for predicting the influence of individuals.
Policy Evaluation and Seeking for Multi-Agent Reinforcement Learning via Best Response
With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Topology Inference with Multivariate Cumulants: The Möbius Inference Algorithm
Many tasks regarding the monitoring, management, and design of communication networks rely on knowledge of the routing topology.
Distributed and time-varying primal-dual dynamics via contraction analysis
In this note, we provide an overarching analysis of primal-dual dynamics associated to linear equality-constrained optimization problems using contraction analysis.
