Image de couverture de livre

Time-optimal neural feedback control of nilpotent systems as a binary classification problem

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Bicego, Sara, Gue, Samuel, Kalise, Dante, Villamizar, Nelly
Format:	Preprint
Publié:	2025
Sujets:	Optimization and Control Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Control of high-dimensional collective dynamics by deep neural feedback laws and kinetic modelling
par: Albi, Giacomo, et autres
Publié: (2024)

Data/moment-driven approaches for fast predictive control of collective dynamics
par: Albi, Giacomo, et autres
Publié: (2024)

Computation and Control of Unstable Steady States for Mean Field Multiagent Systems
par: Bicego, Sara, et autres
Publié: (2024)

Solving a class of stochastic optimal control problems by physics-informed neural networks
par: Jiao, Zhe, et autres
Publié: (2024)

Fast and robust consensus-based optimization via optimal feedback control
par: Huang, Yuyang, et autres
Publié: (2024)

Multi-level Optimal Control with Neural Surrogate Models
par: Kalise, Dante, et autres
Publié: (2024)

Hierarchical clustering and dimensional reduction for optimal control of large-scale agent-based models
par: Monti, Angela, et autres
Publié: (2025)

An analysis of optimization problems involving ReLU neural networks
par: Plate, Christoph, et autres
Publié: (2025)

Formulations and scalability of neural network surrogates in nonlinear optimization problems
par: Parker, Robert B., et autres
Publié: (2024)

A minimax optimal control approach for robust neural ODEs
par: Cipriani, Cristina, et autres
Publié: (2023)

A PDE-constrained Optimization Approach to Optimal Trajectory Planning under Uncertainty via Reflected Schrödinger Bridges
par: Kalise, Dante, et autres
Publié: (2025)

On improving generalization in a class of learning problems with the method of small parameters for weakly-controlled optimal gradient systems
par: Befekadu, Getachew K.
Publié: (2024)

Separable approximations of optimal value functions under a decaying sensitivity assumption
par: Sperl, Mario, et autres
Publié: (2023)

Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
par: Wu, Long, et autres
Publié: (2024)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
par: Dereich, Steffen, et autres
Publié: (2025)

A neural network-based approach to hybrid systems identification for control
par: Fabiani, Filippo, et autres
Publié: (2024)

Inverse classification with logistic and softmax classifiers: efficient optimization
par: Carreira-Perpiñán, Miguel Á., et autres
Publié: (2023)

Efficient model predictive control for nonlinear systems modelled by deep neural networks
par: Lan, Jianglin
Publié: (2024)

On the influence of dependent features in classification problems: a game-theoretic perspective
par: Davila-Pena, Laura, et autres
Publié: (2024)

A successive approximation method in functional spaces for hierarchical optimal control problems and its application to learning
par: Befekadu, Getachew K.
Publié: (2024)

Global optimization of graph acquisition functions for neural architecture search
par: Xie, Yilin, et autres
Publié: (2025)

A constrained optimization approach to improve robustness of neural networks
par: Zhao, Shudian, et autres
Publié: (2024)

On characterizing optimal learning trajectories in a class of learning problems
par: Befekadu, Getachew K
Publié: (2025)

Derivatives of Stochastic Gradient Descent in parametric optimization
par: Iutzeler, Franck, et autres
Publié: (2024)

Proximal basin hopping: global optimization with guarantees
par: Lauga, Guillaume, et autres
Publié: (2026)

Reliably-stabilizing piecewise-affine neural network controllers
par: Fabiani, Filippo, et autres
Publié: (2021)

MIQCQP reformulation of the ReLU neural networks Lipschitz constant estimation problem
par: Sbihi, Mohammed, et autres
Publié: (2024)

A Total Variation Flow Scheme for Ergodic Mean Field Games
par: Kalise, Dante, et autres
Publié: (2024)

Mean-field neural networks-based algorithms for McKean-Vlasov control problems *
par: Pham, Huyên, et autres
Publié: (2022)

Gromov-Wasserstein and optimal transport: from assignment problems to probabilistic numeric
par: Seyedi, Iman, et autres
Publié: (2025)

Sinkhorn algorithms and linear programming solvers for optimal partial transport problems
par: Bai, Yikun
Publié: (2024)

Risk-averse learning with delayed feedback
par: Wang, Siyi, et autres
Publié: (2024)

Sparse stabilization of mean-field agent dynamics through a three-operator splitting method
par: Albi, Giacomo, et autres
Publié: (2025)

An optimal control perspective on diffusion-based generative modeling
par: Berner, Julius, et autres
Publié: (2022)

Derivative-free tree optimization for complex systems
par: Wei, Ye, et autres
Publié: (2024)

Randomized multi-class classification under system constraints: a unified approach via post-processing
par: Chzhen, Evgenii, et autres
Publié: (2025)

Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
par: Tomasetto, Matteo, et autres
Publié: (2024)

Approximate non-linear model predictive control with safety-augmented neural networks
par: Hose, Henrik, et autres
Publié: (2023)

Learning solutions to some toy constrained optimization problems in infinite dimensional Hilbert spaces
par: Mandal, Pinak
Publié: (2024)

Bayesian optimization as a flexible and efficient design framework for sustainable process systems
par: Paulson, Joel A., et autres
Publié: (2024)