Learning Neural Contracting Dynamics: Extended Linearization and Global Guarantees
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | Jaffe, Sean, Davydov, Alexander, Lapsekili, Deniz, Singh, Ambuj, Bullo, Francesco |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Perspectives on Contractivity in Control, Optimization, and Learning
par: Davydov, Alexander, et autres
Publié: (2024)
par: Davydov, Alexander, et autres
Publié: (2024)
Exponential Stability of Parametric Optimization-Based Controllers via Lur'e Contractivity
par: Davydov, Alexander, et autres
Publié: (2024)
par: Davydov, Alexander, et autres
Publié: (2024)
Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks
par: Davydov, Alexander, et autres
Publié: (2021)
par: Davydov, Alexander, et autres
Publié: (2021)
Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
par: Davydov, Alexander
Publié: (2025)
par: Davydov, Alexander
Publié: (2025)
A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
par: Gokhale, Anand, et autres
Publié: (2026)
par: Gokhale, Anand, et autres
Publié: (2026)
Incremental Input-to-State Stability and Equilibrium Tracking for Stochastic Contracting Dynamics
par: Kawano, Yu, et autres
Publié: (2026)
par: Kawano, Yu, et autres
Publié: (2026)
Contractivity Analysis and Control Design for Lur'e Systems: Lipschitz, Incrementally Sector Bounded, and Monotone Nonlinearities
par: Shima, Ryotaro, et autres
Publié: (2025)
par: Shima, Ryotaro, et autres
Publié: (2025)
On Weakly Contracting Dynamics for Convex Optimization
par: Centorrino, Veronica, et autres
Publié: (2024)
par: Centorrino, Veronica, et autres
Publié: (2024)
Proximal Gradient Dynamics: Monotonicity, Exponential Convergence, and Applications
par: Gokhale, Anand, et autres
Publié: (2024)
par: Gokhale, Anand, et autres
Publié: (2024)
Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
par: Li, Haoyu, et autres
Publié: (2025)
par: Li, Haoyu, et autres
Publié: (2025)
Contractivity of Multi-Stage Runge-Kutta Dynamics
par: Kawano, Yu, et autres
Publié: (2026)
par: Kawano, Yu, et autres
Publié: (2026)
Time-Varying Convex Optimization: A Contraction and Equilibrium Tracking Approach
par: Davydov, Alexander, et autres
Publié: (2023)
par: Davydov, Alexander, et autres
Publié: (2023)
Modeling and Contractivity of Neural-Synaptic Networks with Hebbian Learning
par: Centorrino, Veronica, et autres
Publié: (2022)
par: Centorrino, Veronica, et autres
Publié: (2022)
The Yakubovich S-Lemma Revisited: Stability and Contractivity in Non-Euclidean Norms
par: Proskurnikov, Anton V., et autres
Publié: (2022)
par: Proskurnikov, Anton V., et autres
Publié: (2022)
Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
par: Centorrino, Veronica, et autres
Publié: (2025)
par: Centorrino, Veronica, et autres
Publié: (2025)
Solving Inverse Problems with Deep Linear Neural Networks: Global Convergence Guarantees for Gradient Descent with Weight Decay
par: Laus, Hannah, et autres
Publié: (2025)
par: Laus, Hannah, et autres
Publié: (2025)
Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span
par: Chae, Woojin, et autres
Publié: (2024)
par: Chae, Woojin, et autres
Publié: (2024)
Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning
par: Caron, Francois, et autres
Publié: (2023)
par: Caron, Francois, et autres
Publié: (2023)
Non-Euclidean Monotone Operator Theory and Applications
par: Davydov, Alexander, et autres
Publié: (2023)
par: Davydov, Alexander, et autres
Publié: (2023)
Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
par: Agafonov, Artem, et autres
Publié: (2025)
par: Agafonov, Artem, et autres
Publié: (2025)
Contracting Neural Networks: Sharp LMI Conditions with Applications to Integral Control and Deep Learning
par: Gokhale, Anand, et autres
Publié: (2026)
par: Gokhale, Anand, et autres
Publié: (2026)
Offline-Online Reinforcement Learning for Linear Mixture MDPs
par: Zhang, Zhongjun, et autres
Publié: (2026)
par: Zhang, Zhongjun, et autres
Publié: (2026)
A Recovery Guarantee for Sparse Neural Networks
par: Fridovich-Keil, Sara, et autres
Publié: (2025)
par: Fridovich-Keil, Sara, et autres
Publié: (2025)
Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
par: Shi, Kanghong, et autres
Publié: (2025)
par: Shi, Kanghong, et autres
Publié: (2025)
Positive Competitive Networks for Sparse Reconstruction
par: Centorrino, Veronica, et autres
Publié: (2023)
par: Centorrino, Veronica, et autres
Publié: (2023)
Federated Dynamical Low-Rank Training with Global Loss Convergence Guarantees
par: Schotthöfer, Steffen, et autres
Publié: (2024)
par: Schotthöfer, Steffen, et autres
Publié: (2024)
FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
par: Nguyen, Hoang T., et autres
Publié: (2025)
par: Nguyen, Hoang T., et autres
Publié: (2025)
Sampled-data Systems: Stability, Contractivity and Single-iteration Suboptimal MPC
par: Chen, Yiting, et autres
Publié: (2025)
par: Chen, Yiting, et autres
Publié: (2025)
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees
par: Smith, Chandler, et autres
Publié: (2024)
par: Smith, Chandler, et autres
Publié: (2024)
Sharp Global Guarantees for Nonconvex Low-rank Recovery in the Noisy Overparameterized Regime
par: Zhang, Richard Y.
Publié: (2021)
par: Zhang, Richard Y.
Publié: (2021)
Improved Global Guarantees for the Nonconvex Burer--Monteiro Factorization via Rank Overparameterization
par: Zhang, Richard Y.
Publié: (2022)
par: Zhang, Richard Y.
Publié: (2022)
A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
par: Zhou, Yuhao, et autres
Publié: (2025)
par: Zhou, Yuhao, et autres
Publié: (2025)
Learning Linear Dynamics from Bilinear Observations
par: Sattar, Yahya, et autres
Publié: (2024)
par: Sattar, Yahya, et autres
Publié: (2024)
Memory-Reduced Meta-Learning with Guaranteed Convergence
par: Yang, Honglin, et autres
Publié: (2024)
par: Yang, Honglin, et autres
Publié: (2024)
Towards Robust Learning to Optimize with Theoretical Guarantees
par: Song, Qingyu, et autres
Publié: (2025)
par: Song, Qingyu, et autres
Publié: (2025)
On Performance Guarantees for Federated Learning with Personalized Constraints
par: Ebrahimi, Mohammadjavad, et autres
Publié: (2026)
par: Ebrahimi, Mohammadjavad, et autres
Publié: (2026)
Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
par: Qin, Zhen, et autres
Publié: (2023)
par: Qin, Zhen, et autres
Publié: (2023)
A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems
par: Li, Anran, et autres
Publié: (2025)
par: Li, Anran, et autres
Publié: (2025)
A Finite-Time Analysis of TD Learning with Linear Function Approximation without Projections or Strong Convexity
par: Lee, Wei-Cheng, et autres
Publié: (2025)
par: Lee, Wei-Cheng, et autres
Publié: (2025)
Data-Driven Performance Guarantees for Classical and Learned Optimizers
par: Sambharya, Rajiv, et autres
Publié: (2024)
par: Sambharya, Rajiv, et autres
Publié: (2024)
Documents similaires
-
Perspectives on Contractivity in Control, Optimization, and Learning
par: Davydov, Alexander, et autres
Publié: (2024) -
Exponential Stability of Parametric Optimization-Based Controllers via Lur'e Contractivity
par: Davydov, Alexander, et autres
Publié: (2024) -
Non-Euclidean Contraction Analysis of Continuous-Time Neural Networks
par: Davydov, Alexander, et autres
Publié: (2021) -
Verifying Closed-Loop Contractivity of Learning-Based Controllers via Partitioning
par: Davydov, Alexander
Publié: (2025) -
A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
par: Gokhale, Anand, et autres
Publié: (2026)