arXiv Machine Learning
Generalization as a robust performance property of learning-enabled dynamical systems
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Oct 22
A Decade of Learning-Based Control: From Theoretic Foundations to Practical Deployment
This tutorial paper reviews a decade of advances at the intersection of machine learning and control, focusing on methods that blend machine learning flexibility with control theory’s structure to create capable, reliable, and safe autonomous systems. It covers core concepts for stability and safety, learning dynamics models to handle uncertainty, and data‑driven policy synthesis techniques such as reinforcement learning, imitation learning, and Bayesian optimization. Representative applications and open challenges are discussed, and the authors provide implementations, examples, and additional resources on their website.
arXiv Machine Learning
Sep 18
Demystifying Linear Operator Learning for Control Systems
The paper introduces a structured method for learning linear operators in control systems using data. It leverages the framework of (semi)groups for evolution equations to establish structural assumptions and applies inverse‑problems theory to analyze learning algorithms, revealing error decompositions, convergence guarantees, and optimal regularization. Focusing on bounded operators on Hilbert spaces, the authors derive a convergent estimator for time‑varying systems, illustrating the practical power of their approach.
arXiv Machine Learning
Aug 14
On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective
arXiv:2608. 13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency.
arXiv Machine Learning
Jun 29
PAC-Bayesian Certificates for Quadratic Closed-Loop Control
arXiv:2606. 28281v1 Announce Type: cross Abstract: PAC-Bayesian bounds provide finite-sample guarantees for data-dependent randomized predictors, but applying them to learning-based control is difficult because the natural objective is a quadratic trajectory cost.
arXiv Machine Learning
Jul 3
A Unified Lyapunov-IQC Framework for Uniform Stability of Smooth Quadratic First-Order Accelerated Optimizers
arXiv:2605. 08488v2 Announce Type: replace-cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization algorithms in the $\beta$-smooth and $\gamma$-strongly convex regime.
arXiv Machine Learning
Jun 24
Hessian-augmented Supervised Learning for Hamilton-Jacobi-Bellman PDEs
arXiv:2606. 23827v1 Announce Type: cross Abstract: A data-driven method is developed for approximating value functions in deterministic optimal control problems with nonlinear control-affine dynamics.