Imagen de Portada

Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Entesari, Taha, Fazlyab, Mahyar
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Systems and Control
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Compositional Curvature Bounds for Deep Neural Networks
por: Entesari, Taha, et al.
Publicado: (2024)

Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
por: Sharifi, Sina, et al.
Publicado: (2024)

Verification-Aided Learning of Neural Network Barrier Functions with Termination Guarantees
por: Chen, Shaoru, et al.
Publicado: (2024)

Actor-Critic Physics-informed Neural Lyapunov Control
por: Wang, Jiarui, et al.
Publicado: (2024)

Learning Performance-Oriented Control Barrier Functions Under Complex Safety Constraints and Limited Actuation
por: Manda, Lakshmideepakreddy, et al.
Publicado: (2024)

Sequential QCQP for Bilevel Optimization with Line Search
por: Sharifi, Sina, et al.
Publicado: (2025)

Domain Adaptive Safety Filters via Deep Operator Learning
por: Manda, Lakshmideepakreddy, et al.
Publicado: (2024)

Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization
por: Fazlyab, Mahyar, et al.
Publicado: (2023)

Safe Gradient Flow for Bilevel Optimization
por: Sharifi, Sina, et al.
Publicado: (2025)

LDLT $\mathcal{L}$-Lipschitz Network: Generalized Deep End-To-End Lipschitz Network Construction
por: Juston, Marius F. R., et al.
Publicado: (2025)

Constrained Entropic Unlearning: A Primal-Dual Framework for Large Language Models
por: Entesari, Taha, et al.
Publicado: (2025)

End-to-End Learning Framework for Solving Non-Markovian Optimal Control
por: Zhang, Xiaole, et al.
Publicado: (2025)

Multi-Resolution End-to-End Deep Neural Network for Optimizing Latency-Accuracy Tradeoff in Autonomous Driving
por: Weng, Qitao, et al.
Publicado: (2026)

Gradient-Regularized Out-of-Distribution Detection
por: Sharifi, Sina, et al.
Publicado: (2024)

Anytime-Feasible First-Order Optimization via Safe Sequential QCQP
por: Wang, Jiarui, et al.
Publicado: (2025)

End-to-End Reinforcement Learning of Koopman Models for Economic Nonlinear Model Predictive Control
por: Mayfrank, Daniel, et al.
Publicado: (2023)

Nonparametric End-to-End Probabilistic Forecasting of Distributed Generation Outputs Considering Missing Data Imputation
por: Chen, Minghui, et al.
Publicado: (2024)

An End-to-End Approach for Microgrid Probabilistic Forecasting and Robust Operation via Decision-focused Learning
por: Cao, Tingwei, et al.
Publicado: (2025)

End-to-End Reinforcement Learning for Torque Based Variable Height Hopping
por: Soni, Raghav, et al.
Publicado: (2023)

Sample-Free Safety Assessment of Neural Network Controllers via Taylor Methods
por: Evans, Adam, et al.
Publicado: (2026)

Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation
por: Hu, Hanjiang, et al.
Publicado: (2024)

Correctness Verification of Neural Networks Approximating Differential Equations
por: Ellinas, Petros, et al.
Publicado: (2024)

Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
por: Vertovec, Nikolaus, et al.
Publicado: (2025)

End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
por: Wolf, Hinrikus, et al.
Publicado: (2024)

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

Physics-Informed Neural Network Lyapunov Functions: PDE Characterization, Learning, and Verification
por: Liu, Jun, et al.
Publicado: (2023)

Generalized Model Predictive Path Integral Control as Expectation--Maximization
por: Wang, Jiarui, et al.
Publicado: (2026)

Model Predictive Path Integral Control as Preconditioned Gradient Descent
por: Fazlyab, Mahyar, et al.
Publicado: (2026)

LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
por: Naug, Avisek, et al.
Publicado: (2025)

Hierarchical Upper Confidence Bounds for Constrained Online Learning
por: Baheri, Ali
Publicado: (2024)

On-Demand Video Dispatch Networks: A Scalable End-to-End Learning Approach
por: Yang, Damao, et al.
Publicado: (2018)

Provably Bounding Neural Network Preimages
por: Kotha, Suhas, et al.
Publicado: (2023)

Analytical Verification of Performance of Deep Neural Network Based Time-Synchronized Distribution System State Estimation
por: Azimian, Behrouz, et al.
Publicado: (2023)

End-to-end guarantees for indirect data-driven control of bilinear systems with finite stochastic data
por: Chatzikiriakos, Nicolas, et al.
Publicado: (2024)

N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network
por: Pham, Thuan, 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 Surrogate Verification of Image-based Neural Network Control Systems using Composition and Unrolling
por: Cai, Feiyang, et al.
Publicado: (2024)

Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates
por: Badings, Thom, et al.
Publicado: (2024)

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

Whole-body End-Effector Pose Tracking
por: Portela, Tifanny, et al.
Publicado: (2024)