Image de couverture de livre

Restarted contractive operators to learn at equilibrium

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Davy, Leo, Briceno-Arias, Luis M., Pustelnik, N.
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

Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
par: Chen, Xin, et autres
Publié: (2025)

A flexible block-coordinate forward-backward algorithm for non-smooth and non-convex optimization
par: Briceño-Arias, Luis, et autres
Publié: (2025)

A learning-based mathematical programming formulation for the automatic configuration of optimization solvers
par: Iommazzo, Gabriele, et autres
Publié: (2024)

Linear quadratic control of nonlinear systems with Koopman operator learning and the Nyström method
par: Caldarelli, Edoardo, et autres
Publié: (2024)

Local convergence of simultaneous min-max algorithms to differential equilibrium on Riemannian manifold
par: Zhang, Sixin
Publié: (2024)

Stochastic set-valued optimization and its application to robust learning
par: Giovannelli, Tommaso, et autres
Publié: (2026)

Robotic warehousing operations: a learn-then-optimize approach to large-scale neighborhood search
par: Barnhart, Cynthia, et autres
Publié: (2024)

CLASP: An online learning algorithm for Convex Losses And Squared Penalties
par: Ferreira, Ricardo N., et autres
Publié: (2026)

Lyapunov analysis for FISTA under strong convexity
par: Briceño-Arias, Luis M.
Publié: (2025)

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

Spherical Harmonic Optimal Transport: Application to Climate Models Comparisons
par: Houédry, Pierre, et autres
Publié: (2026)

Convergence of optimizers implies eigenvalues filtering at equilibrium
par: Bolte, Jerome, et autres
Publié: (2025)

Data augmentation for machine learning of chemical process flowsheets
par: Balhorn, Lukas Schulze, et autres
Publié: (2023)

Learning to Configure Mathematical Programming Solvers by Mathematical Programming
par: Iommazzo, Gabriele, et autres
Publié: (2024)

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
par: Stops, Laura, et autres
Publié: (2022)

A Bregman firmly nonexpansive proximal operator for baryconvex optimization
par: Achab, Mastane
Publié: (2024)

Decision-calibrated prediction sets for robust power system operations
par: Stratigakos, Akylas, et autres
Publié: (2026)

Structured Reinforcement Learning for Combinatorial Decision-Making
par: Hoppe, Heiko, et autres
Publié: (2025)

A reinforcement learning agent for maintenance of deteriorating systems with increasingly imperfect repairs
par: Marugán, Alberto Pliego, et autres
Publié: (2025)

The impact of machine learning forecasting on strategic decision-making for Bike Sharing Systems
par: Angelelli, Enrico, et autres
Publié: (2026)

Sparse Polyak with optimal thresholding operators for high-dimensional M-estimation
par: Qiao, Tianqi, et autres
Publié: (2025)

What is the objective of reasoning with reinforcement learning?
par: Davis, Damek, et autres
Publié: (2025)

Distributed optimization: designed for federated learning
par: Guo, Wenyou, et autres
Publié: (2025)

Meta-reinforcement learning with minimum attention
par: Gupta, Shashank, et autres
Publié: (2025)

Finite sample learning of moving targets
par: Vertovec, Nikolaus, et autres
Publié: (2024)

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

Assured autonomy: How operations research powers and orchestrates generative AI systems
par: Dai, Tinglong, et autres
Publié: (2025)

Meta-learning for sample-efficient Bayesian optimisation of fed-batch processes
par: Langdon, Becky, et autres
Publié: (2026)

Contractivity and linear convergence in bilinear saddle-point problems: An operator-theoretic approach
par: Dirren, Colin, et autres
Publié: (2024)

Finite-sample guarantees for data-driven forward-backward operator methods
par: Fabiani, Filippo, et autres
Publié: (2025)

A survey on secure decentralized optimization and learning
par: Liu, Changxin, et autres
Publié: (2024)

Regularized Q-learning through Robust Averaging
par: Schmitt-Förster, Peter, et autres
Publié: (2024)

Apprenticeship learning with prior beliefs using inverse optimization
par: Junca, Mauricio, et autres
Publié: (2025)

Convergence of gradient flow for learning convolutional neural networks
par: Diederen, Jona-Maria, et autres
Publié: (2026)

How to unlearn a learned Machine Learning model ?
par: Achour, Seifeddine
Publié: (2024)

Novel clustered federated learning based on local loss
par: Gu, Endong, et autres
Publié: (2024)

A brief note on learning problem with global perspectives
par: Befekadu, Getachew K.
Publié: (2026)

A primal-dual perspective for distributed TD-learning
par: Lim, Han-Dong, et autres
Publié: (2023)

DistrictNet: Decision-aware learning for geographical districting
par: Ahmed, Cheikh, et autres
Publié: (2024)

Alignment of large language models with constrained learning
par: Zhang, Botong, et autres
Publié: (2025)