Imagen de Portada

Bridging Control with Neural Network Verifier alpha-beta-CROWN: A Tutorial

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Li, Haoyu, Zhong, Xiangru, Cheng, Hao, Hu, Bin, Zhang, Huan
Formato:	Preprint
Publicado:	2026
Materias:	Systems and Control Artificial Intelligence Machine Learning Optimization and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
por: Li, Haoyu, et al.
Publicado: (2025)

Efficient Reachability Analysis for Convolutional Neural Networks Using Hybrid Zonotopes
por: Zhang, Yuhao, et al.
Publicado: (2025)

On the Hopf-Cole Transform for Control-affine Schrödinger Bridge
por: Teter, Alexis, et al.
Publicado: (2025)

Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulation
por: Yang, Lujie, et al.
Publicado: (2024)

ControlAgent: Automating Control System Design via Novel Integration of LLM Agents and Domain Expertise
por: Guo, Xingang, et al.
Publicado: (2024)

Lyapunov Function Consistent Adaptive Network Signal Control with Back Pressure and Reinforcement Learning
por: Ma, Chaolun, et al.
Publicado: (2022)

Optimal Control Operator Perspective and a Neural Adaptive Spectral Method
por: Feng, Mingquan, et al.
Publicado: (2024)

Nonlinear Non-Gaussian Density Steering with Input and Noise Channel Mismatch: Sinkhorn with Memory for Solving the Control-affine Schrödinger Bridge Problem
por: Bondar, Georgiy A., et al.
Publicado: (2026)

On the Convergence of Overparameterized Problems: Inherent Properties of the Compositional Structure of Neural Networks
por: de Oliveira, Arthur Castello Branco, et al.
Publicado: (2025)

Two-Stage Learning of Stabilizing Neural Controllers via Zubov Sampling and Iterative Domain Expansion
por: Li, Haoyu, et al.
Publicado: (2025)

A Riemannian Optimization Perspective of the Gauss-Newton Method for Feedforward Neural Networks
por: Cayci, Semih
Publicado: (2024)

Optimizing Inventory Routing: A Decision-Focused Learning Approach using Neural Networks
por: Islam, MD Shafikul, et al.
Publicado: (2023)

Distributionally Robust Safety Verification of Neural Networks via Worst-Case CVaR
por: Kishida, Masako
Publicado: (2025)

Analytic Bridge Diffusions for Controlled Path Generation
por: Chertkov, Michael
Publicado: (2026)

Stochastic Learning of Computational Resource Usage as Graph Structured Multimarginal Schrödinger Bridge
por: Bondar, Georgiy A., et al.
Publicado: (2024)

Adaptive Neural-Operator Backstepping Control of a Benchmark Hyperbolic PDE
por: Lamarque, Maxence, et al.
Publicado: (2024)

Revisiting LQR Control from the Perspective of Receding-Horizon Policy Gradient
por: Zhang, Xiangyuan, et al.
Publicado: (2023)

Parallel Differentiable Reachability for Learning and Planning with Certified Neural Dynamics and Controllers
por: Shen, Keyi, et al.
Publicado: (2026)

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
por: Tsukamoto, Hiroyasu, et al.
Publicado: (2022)

Improved Scalable Lipschitz Bounds for Deep Neural Networks
por: Syed, Usman, et al.
Publicado: (2025)

Formally Verified Physics-Informed Neural Control Lyapunov Functions
por: Liu, Jun, et al.
Publicado: (2024)

Co-Learning Port-Hamiltonian Systems and Optimal Energy-Shaping Control
por: Kamboj, Ankur, et al.
Publicado: (2026)

Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
por: Jing, Gangshan, et al.
Publicado: (2021)

Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
por: Skifstad, Julian, et al.
Publicado: (2026)

Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verification
por: Zhou, Duo, et al.
Publicado: (2025)

Intersection of Reinforcement Learning and Bayesian Optimization for Intelligent Control of Industrial Processes: A Safe MPC-based DPG using Multi-Objective BO
por: Esfahani, Hossein Nejatbakhsh, et al.
Publicado: (2025)

Multi-Year Maintenance Planning for Large-Scale Infrastructure Systems: A Novel Network Deep Q-Learning Approach
por: Fard, Amir, et al.
Publicado: (2025)

Gain Scheduling with a Neural Operator for a Transport PDE with Nonlinear Recirculation
por: Lamarque, Maxence, et al.
Publicado: (2024)

Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes
por: Faradonbeh, Mohamad Kazem Shirani, et al.
Publicado: (2022)

T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method
por: Zhu, Haoyu, et al.
Publicado: (2025)

Convergence and sample complexity of natural policy gradient primal-dual methods for constrained MDPs
por: Ding, Dongsheng, et al.
Publicado: (2022)

Robust Stability of Neural Network Control Systems with Interval Matrix Uncertainties
por: Zhang, Yuhao, et al.
Publicado: (2023)

Tutorial on amortized optimization
por: Amos, Brandon
Publicado: (2022)

Stability and Performance Analysis of Discrete-Time ReLU Recurrent Neural Networks
por: Noori, Sahel Vahedi, et al.
Publicado: (2024)

Verifiable Error Bounds for Physics-Informed Neural Network Solutions of Lyapunov and Hamilton-Jacobi-Bellman Equations
por: Liu, Jun
Publicado: (2026)

Scalable Data-Driven Reachability Analysis and Control via Koopman Operators with Conformal Coverage Guarantees
por: Nath, Devesh, et al.
Publicado: (2026)

Benchmarking Reinforcement Learning via Stochastic Converse Optimality: Generating Systems with Known Optimal Policies
por: Ibrahim, Sinan, et al.
Publicado: (2026)

Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
por: Ding, Jianglin, et al.
Publicado: (2025)

Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling
por: Zhu, Feng, et al.
Publicado: (2025)

WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
por: Suri, Dhruv, et al.
Publicado: (2026)