:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Xavier, Daniel Martin, Chamoin, Ludovic, Jerray, Jawher, Fribourg, Laurent
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Systems and Control
Online Access:	https://arxiv.org/abs/2411.18806
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Rademacher Complexity of Neural ODEs via Chen-Fliess Series
by: Hanson, Joshua, et al.
Published: (2024)

Transport Map Coupling Filter for State-Parameter Estimation
by: Grashorn, Jan, et al.
Published: (2024)

Complex-Value Spatio-temporal Graph Convolutional Neural Networks and its Applications to Electric Power Systems AI
by: Wu, Tong, et al.
Published: (2022)

Scalable Kernel Inverse Optimization
by: Long, Youyuan, et al.
Published: (2024)

Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing
by: Puthumanaillam, Gokul, et al.
Published: (2025)

Koopman Kernel Regression
by: Bevanda, Petar, et al.
Published: (2023)

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

Game-Theory-Assisted Reinforcement Learning for Border Defense: Early Termination based on Analytical Solutions
by: Das, Goutam, et al.
Published: (2026)

Fast Robust Kernel Regression through Sign Gradient Descent with Early Stopping
by: Allerbo, Oskar
Published: (2023)

Kernel Sum of Squares for Data Adapted Kernel Learning of Dynamical Systems from Data: A global optimization approach
by: Lengyel, Daniel, et al.
Published: (2024)

Universal Approximation Power of Deep Residual Neural Networks via Nonlinear Control Theory
by: Tabuada, Paulo, et al.
Published: (2020)

Change Detection of Markov Kernels with Unknown Pre and Post Change Kernel
by: Chen, Hao, et al.
Published: (2022)

Promises of Deep Kernel Learning for Control Synthesis
by: Reed, Robert, et al.
Published: (2023)

The Stochastic Occupation Kernel Method for System Identification
by: Wells, Michael, et al.
Published: (2024)

A Nonlinear Separation Principle via Contraction Theory: Applications to Neural Networks, Control, and Learning
by: Gokhale, Anand, et al.
Published: (2026)

Enhancing Reinforcement Learning Agents with Local Guides
by: Daoudi, Paul, et al.
Published: (2024)

State Derivative Normalization for Continuous-Time Deep Neural Networks
by: Weigand, Jonas, et al.
Published: (2024)

Wiener Chaos in Kernel Regression: Towards Untangling Aleatoric and Epistemic Uncertainty
by: Faulwasser, T., et al.
Published: (2023)

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

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

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
by: Saldi, Naci, et al.
Published: (2025)

Data-driven Nonlinear Model Reduction using Koopman Theory: Integrated Control Form and NMPC Case Study
by: Schulze, Jan C., et al.
Published: (2024)

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

Convergence analysis of wide shallow neural operators within the framework of Neural Tangent Kernel
by: Xu, Xianliang, et al.
Published: (2024)

LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers
by: Pauli, Patricia, et al.
Published: (2024)

Power Failure Cascade Prediction using Graph Neural Networks
by: Chadaga, Sathwik, et al.
Published: (2024)

Koopman Subspace Pruning in Reproducing Kernel Hilbert Spaces via Principal Vectors
by: Shah, Dhruv, et al.
Published: (2026)

Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark
by: Daoudi, Paul, et al.
Published: (2024)

On the Sample Complexity of Imitation Learning for Smoothed Model Predictive Control
by: Pfrommer, Daniel, et al.
Published: (2023)

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

Online Regularized Statistical Learning in Reproducing Kernel Hilbert Space With Non-Stationary Data
by: Zhang, Xiwei, et al.
Published: (2024)

Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks
by: Wei, Lai, et al.
Published: (2021)

Improved Sample Complexity of Imitation Learning for Barrier Model Predictive Control
by: Pfrommer, Daniel, et al.
Published: (2024)

Learning of Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces
by: Smith, Torbjørn, et al.
Published: (2023)

Analysis of Off-Policy Multi-Step TD-Learning with Linear Function Approximation
by: Lee, Donghwan
Published: (2024)

Robust Power System State Estimation using Physics-Informed Neural Networks
by: Falas, Solon, et al.
Published: (2025)

Kernel-Based Sparse Additive Nonlinear Model Structure Detection through a Linearization Approach
by: Ebrahimkhani, Sadegh, et al.
Published: (2025)

Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces
by: Hassan, Noha, et al.
Published: (2026)

Component-Aware Pruning Framework for Neural Network Controllers via Gradient-Based Importance Estimation
by: Sundaram, Ganesh, et al.
Published: (2026)

Learning to optimize with convergence guarantees using nonlinear system theory
by: Martin, Andrea, et al.
Published: (2024)