Cover Image

Promises of Deep Kernel Learning for Control Synthesis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Reed, Robert, Laurenti, Luca, Lahijanian, Morteza
Format:	Preprint
Published:	2023
Subjects:	Systems and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning-Based Shielding for Safe Autonomy under Unknown Dynamics
by: Reed, Robert, et al.
Published: (2024)

A Unifying Perspective for Safety of Stochastic Systems: From Barrier Functions to Finite Abstractions
by: Laurenti, Luca, et al.
Published: (2023)

Data-Driven Permissible Safe Control with Barrier Certificates
by: Mazouz, Rayan, et al.
Published: (2024)

Piecewise Control Barrier Functions for Stochastic Systems
by: Mazouz, Rayan, et al.
Published: (2025)

Learning Markov Processes as Sum-of-Square Forms for Analytical Belief Propagation
by: Amorese, Peter, et al.
Published: (2026)

Universal Learning of Stochastic Dynamics for Exact Belief Propagation using Bernstein Normalizing Flows
by: Amorese, Peter, et al.
Published: (2025)

Error Bounds For Gaussian Process Regression Under Bounded Support Noise With Applications To Safety Certification
by: Reed, Robert, et al.
Published: (2024)

Verification of Unknown Dynamical Systems via Autoencoder Latent Space
by: Reed, Robert, et al.
Published: (2025)

Time-Varying Reach-Avoid Control Certificates for Stochastic Systems
by: Mazouz, Rayan, et al.
Published: (2026)

Data-Driven Strategy Synthesis for Stochastic Systems with Unknown Nonlinear Disturbances
by: Gracia, Ibon, et al.
Published: (2024)

Error Bounds for Physics-Informed Neural Networks in Fokker-Planck PDEs
by: Kong, Chun-Wei, et al.
Published: (2024)

StochasticBarrier.jl: A Toolbox for Stochastic Barrier Function Synthesis
by: Mazouz, Rayan, et al.
Published: (2026)

Uncertainty Propagation in Stochastic Systems via Mixture Models with Error Quantification
by: Figueiredo, Eduardo, et al.
Published: (2024)

Efficiency through Uncertainty: Scalable Formal Synthesis for Stochastic Hybrid Systems
by: Cauchi, Nathalie, et al.
Published: (2019)

Efficient Strategy Synthesis for Switched Stochastic Systems with Distributional Uncertainty
by: Gracia, Ibon, et al.
Published: (2022)

IntervalMDP.jl: Accelerated Value Iteration for Interval Markov Decision Processes
by: Mathiesen, Frederik Baymler, et al.
Published: (2024)

Temporal Logic Control for Nonlinear Stochastic Systems Under Unknown Disturbances
by: Gracia, Ibon, et al.
Published: (2024)

Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression
by: Skovbekk, John, et al.
Published: (2021)

Provably Safe Motion Planning Under Unknown Disturbances
by: Gracia, Ibon, et al.
Published: (2026)

Interval Markov Decision Processes with Continuous Action-Spaces
by: Delimpaltadakis, Giannis, et al.
Published: (2022)

Shielded Deep Reinforcement Learning for Complex Spacecraft Tasking
by: Reed, Robert, et al.
Published: (2024)

Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
by: Mazouz, Rayan, et al.
Published: (2022)

Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics
by: Betteti, Simone, et al.
Published: (2026)

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

Data-Driven Control via Conditional Mean Embeddings: Formal Guarantees via Uncertain MDP Abstraction
by: Gracia, Ibon, et al.
Published: (2025)

Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods
by: Yazdannik, Saman, et al.
Published: (2023)

Learning Nonlinear Continuous-Time Systems for Formal Uncertainty Propagation and Probabilistic Evaluation
by: Amorese, Peter, et al.
Published: (2026)

Kernel-Based Safe Exploration in Deep Reinforcement Learning
by: Majumdar, Rupak, et al.
Published: (2026)

Certified Neural Approximations of Nonlinear Dynamics
by: Mathiesen, Frederik Baymler, et al.
Published: (2025)

On Polynomial Stochastic Barrier Functions: Bernstein Versus Sum-of-Squares
by: Amorese, Peter, et al.
Published: (2025)

Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
by: Adams, Steven, et al.
Published: (2024)

Interactive Trajectory Planning with Learning-based Distributionally Robust Model Predictive Control and Markov Systems
by: Börve, Erik, et al.
Published: (2026)

On the Optimality of Uncertain MDP Abstractions
by: Gracia, Ibon, et al.
Published: (2026)

Beyond Interval MDPs: Tight and Efficient Abstractions of Stochastic Systems
by: Gracia, Ibon, et al.
Published: (2025)

Safe and High-Performance Learning of Model Predicitve Control using Kernel-Based Interpolation
by: Rose, Alexander, et al.
Published: (2024)

Nuclear Microreactor Control with Deep Reinforcement Learning
by: Tunkle, Leo, et al.
Published: (2025)

An IoT Framework for Building Energy Optimization Using Machine Learning-based MPC
by: Morteza, Aryan, et al.
Published: (2024)

Data-Driven Stochastic Optimal Control in Reproducing Kernel Hilbert Spaces
by: Hoischen, Nicolas, et al.
Published: (2024)

Kernel-Based Optimal Control: An Infinitesimal Generator Approach
by: Bevanda, Petar, et al.
Published: (2024)

Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization
by: Ren, Zelin, et al.
Published: (2021)