:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Hou, Boya, Raginsky, Maxim
Format:	Preprint
Published:	2026
Subjects:	Systems and Control Machine Learning
Online Access:	https://arxiv.org/abs/2605.07052
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

Expressivity of Quadratic Neural ODEs
by: Hanson, Joshua, et al.
Published: (2025)

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

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

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

Spatially-Coupled Network RNA Velocities: A Control-Theoretic Perspective
by: Hou, Boya, et al.
Published: (2026)

Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features
by: Smith, Torbjørn, et al.
Published: (2024)

Separating Geometry from Probability in the Analysis of Generalization
by: Raginsky, Maxim, et al.
Published: (2026)

Function approximation by neural nets in the mean-field regime: Entropic regularization and controlled McKean-Vlasov dynamics
by: Tzen, Belinda, et al.
Published: (2020)

Robust Nonlinear System Identification in Reproducing Kernel Hilbert Spaces via Scenario Optimization
by: Lübsen, Jannis, et al.
Published: (2026)

Revisiting Stochastic Realization Theory using Functional Itô Calculus
by: Veeravalli, Tanya, et al.
Published: (2024)

Data-Driven Observability Analysis for Nonlinear Stochastic Systems
by: Massiani, Pierre-François, et al.
Published: (2023)

Data-Driven Motion Planning for Uncertain Nonlinear Systems
by: Esmaeili, Babak, et al.
Published: (2025)

A Data-Integrated Framework for Learning Fractional-Order Nonlinear Dynamical Systems
by: Yaghooti, Bahram, et al.
Published: (2025)

Control Occupation Kernel Regression for Nonlinear Control-Affine Systems
by: Abudia, Moad, et al.
Published: (2021)

Nonparametric Sparse Online Learning of the Koopman Operator
by: Hou, Boya, et al.
Published: (2025)

A Framework for Adaptive Stabilisation of Nonlinear Stochastic Systems
by: Siriya, Seth, et al.
Published: (2025)

Technical Report for Dissipativity Learning in Reproducing Kernel Hilbert Space
by: Ye, Xiuzhen, et al.
Published: (2025)

On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
by: Rajkumar, Santosh Mohan, et al.
Published: (2026)

Space-Filling Regularization for Robust and Interpretable Nonlinear State Space Models
by: Klein, Hermann, et al.
Published: (2025)

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

State-Space Kolmogorov Arnold Networks for Interpretable Nonlinear System Identification
by: Cruz, Gonçalo Granjal, et al.
Published: (2025)

Learning Linearized Models from Nonlinear Systems under Initialization Constraints with Finite Data
by: Xin, Lei, et al.
Published: (2025)

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)

Koopman-based Estimation of Lyapunov Functions: Theory on a Reproducing Kernel Hilbert Space
by: Tang, Wentao, et al.
Published: (2026)

Bayesian Transfer Operators in Reproducing Kernel Hilbert Spaces
by: Boshoff, Septimus, et al.
Published: (2025)

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

Mirror Descent on Reproducing Kernel Banach Spaces
by: Kumar, Akash, et al.
Published: (2024)

Data-Driven Adversarial Online Control for Unknown Linear Systems
by: Liu, Zishun, et al.
Published: (2023)

CommonPower: A Framework for Safe Data-Driven Smart Grid Control
by: Eichelbeck, Michael, et al.
Published: (2024)

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

Symplectic Inductive Bias for Data-Driven Target Reachability in Hamiltonian Systems
by: Ouyang, Zhuo, et al.
Published: (2026)

Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control
by: Jian, Tong, et al.
Published: (2026)

Federated Nonlinear System Identification
by: Tupe, Omkar, et al.
Published: (2025)

Condition Monitoring with Machine Learning: A Data-Driven Framework for Quantifying Wind Turbine Energy Loss
by: Buchberg, Emil Marcus, et al.
Published: (2025)

Neural Operators for Predictor Feedback Control of Nonlinear Delay Systems
by: Bhan, Luke, et al.
Published: (2024)

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

Fine-Tuned Language Models as Space Systems Controllers
by: Zucchelli, Enrico M., et al.
Published: (2025)

Data-Driven Prediction and Control of Hammerstein-Wiener Systems with Implicit Gaussian Processes
by: Yin, Mingzhou, et al.
Published: (2025)