Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Fuente:
arXiv
Saved in:
| Main Authors: | Adams, Steven, Patanè, Andrea, Lahijanian, Morteza, Laurenti, Luca |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
by: Reed, Robert, et al.
Published: (2024)
by: Reed, Robert, et al.
Published: (2024)
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
by: Kong, Chun-Wei, et al.
Published: (2024)
by: Kong, Chun-Wei, et al.
Published: (2024)
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
by: Figueiredo, Eduardo, et al.
Published: (2024)
by: Figueiredo, Eduardo, et al.
Published: (2024)
Efficient Distribution Learning with Error Bounds in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2026)
by: Figueiredo, Eduardo, et al.
Published: (2026)
Verification of Unknown Dynamical Systems via Autoencoder Latent Space
by: Reed, Robert, et al.
Published: (2025)
by: Reed, Robert, et al.
Published: (2025)
discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
by: Adams, Steven, et al.
Published: (2025)
by: Adams, Steven, et al.
Published: (2025)
Promises of Deep Kernel Learning for Control Synthesis
by: Reed, Robert, et al.
Published: (2023)
by: Reed, Robert, et al.
Published: (2023)
A Unifying Perspective for Safety of Stochastic Systems: From Barrier Functions to Finite Abstractions
by: Laurenti, Luca, et al.
Published: (2023)
by: Laurenti, Luca, et al.
Published: (2023)
On the Robustness of Bayesian Neural Networks to Adversarial Attacks
by: Bortolussi, Luca, et al.
Published: (2022)
by: Bortolussi, Luca, et al.
Published: (2022)
Adaptive Prior Selection in Gaussian Process Bandits with Thompson Sampling
by: Sandberg, Jack, et al.
Published: (2025)
by: Sandberg, Jack, et al.
Published: (2025)
Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation
by: Amorese, Peter, et al.
Published: (2026)
by: Amorese, Peter, et al.
Published: (2026)
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression
by: Skovbekk, John, et al.
Published: (2021)
by: Skovbekk, John, et al.
Published: (2021)
Data-Driven Permissible Safe Control with Barrier Certificates
by: Mazouz, Rayan, et al.
Published: (2024)
by: Mazouz, Rayan, et al.
Published: (2024)
Provably Safe Motion Planning Under Unknown Disturbances
by: Gracia, Ibon, et al.
Published: (2026)
by: Gracia, Ibon, et al.
Published: (2026)
Learning-Based Shielding for Safe Autonomy under Unknown Dynamics
by: Reed, Robert, et al.
Published: (2024)
by: Reed, Robert, et al.
Published: (2024)
Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows
by: Amorese, Peter, et al.
Published: (2025)
by: Amorese, Peter, et al.
Published: (2025)
IntervalMDP.jl: Accelerated Value Iteration for Interval Markov Decision Processes
by: Mathiesen, Frederik Baymler, et al.
Published: (2024)
by: Mathiesen, Frederik Baymler, et al.
Published: (2024)
Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
by: Betteti, Simone, et al.
Published: (2026)
by: Betteti, Simone, et al.
Published: (2026)
Shielded Deep Reinforcement Learning for Complex Spacecraft Tasking
by: Reed, Robert, et al.
Published: (2024)
by: Reed, Robert, et al.
Published: (2024)
Flow-Induced Diagonal Gaussian Processes
by: Lin, Moule, et al.
Published: (2025)
by: Lin, Moule, et al.
Published: (2025)
Provably Bounding Neural Network Preimages
by: Kotha, Suhas, et al.
Published: (2023)
by: Kotha, Suhas, et al.
Published: (2023)
Piecewise Control Barrier Functions for Stochastic Systems
by: Mazouz, Rayan, et al.
Published: (2025)
by: Mazouz, Rayan, et al.
Published: (2025)
Stochastic Barrier Certificates in the Presence of Dynamic Obstacles
by: Mazouz, Rayan, et al.
Published: (2026)
by: Mazouz, Rayan, et al.
Published: (2026)
Time-Varying Reach-Avoid Control Certificates for Stochastic Systems
by: Mazouz, Rayan, et al.
Published: (2026)
by: Mazouz, Rayan, et al.
Published: (2026)
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
by: Mazouz, Rayan, et al.
Published: (2022)
by: Mazouz, Rayan, et al.
Published: (2022)
Interval Markov Decision Processes with Continuous Action-Spaces
by: Delimpaltadakis, Giannis, et al.
Published: (2022)
by: Delimpaltadakis, Giannis, et al.
Published: (2022)
Provable Bounds on the Hessian of Neural Networks: Derivative-Preserving Reachability Analysis
by: Sharifi, Sina, et al.
Published: (2024)
by: Sharifi, Sina, et al.
Published: (2024)
Stochastic Weight Sharing for Bayesian Neural Networks
by: Lin, Moule, et al.
Published: (2025)
by: Lin, Moule, et al.
Published: (2025)
Finite-Time Error Bounds for Greedy-GQ
by: Wang, Yue, et al.
Published: (2022)
by: Wang, Yue, et al.
Published: (2022)
Structured Diffusion Models with Mixture of Gaussians as Prior Distribution
by: Jia, Nanshan, et al.
Published: (2024)
by: Jia, Nanshan, et al.
Published: (2024)
Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control
by: Lederer, Armin, et al.
Published: (2019)
by: Lederer, Armin, et al.
Published: (2019)
Provable Generalization Bounds for Deep Neural Networks with Momentum-Adaptive Gradient Dropout
by: Safder, Adeel
Published: (2025)
by: Safder, Adeel
Published: (2025)
Scalable Verification of Neural Control Barrier Functions Using Linear Bound Propagation
by: Vertovec, Nikolaus, et al.
Published: (2025)
by: Vertovec, Nikolaus, et al.
Published: (2025)
Theoretical Error Analysis of Entropy Approximation for Gaussian Mixtures
by: Furuya, Takashi, et al.
Published: (2022)
by: Furuya, Takashi, et al.
Published: (2022)
GGMPs: Generalized Gaussian Mixture Processes
by: Tekriwal, Vardaan, et al.
Published: (2026)
by: Tekriwal, Vardaan, et al.
Published: (2026)
Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances
by: Gracia, Ibon, et al.
Published: (2024)
by: Gracia, Ibon, et al.
Published: (2024)
Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
by: Boetius, David, et al.
Published: (2026)
by: Boetius, David, et al.
Published: (2026)
Adaptive Soft Error Protection for Neural Network Processing
by: Xue, Xinghua, et al.
Published: (2024)
by: Xue, Xinghua, et al.
Published: (2024)
Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
by: Marzari, Luca, et al.
Published: (2023)
by: Marzari, Luca, et al.
Published: (2023)
Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation
by: Ha, Huong, et al.
Published: (2023)
by: Ha, Huong, et al.
Published: (2023)
Similar Items
-
Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
by: Reed, Robert, et al.
Published: (2024) -
Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
by: Kong, Chun-Wei, et al.
Published: (2024) -
Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
by: Figueiredo, Eduardo, et al.
Published: (2024) -
Efficient Distribution Learning with Error Bounds in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2026) -
Verification of Unknown Dynamical Systems via Autoencoder Latent Space
by: Reed, Robert, et al.
Published: (2025)