:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Ye, Xiaojing
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2603.18387
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning to Configure Mathematical Programming Solvers by Mathematical Programming
by: Iommazzo, Gabriele, et al.
Published: (2024)

Maximum Principle of Optimal Probability Density Control
by: Gaby, Nathan, et al.
Published: (2025)

On the Foundation of Distributionally Robust Reinforcement Learning
by: Wang, Shengbo, et al.
Published: (2023)

Gradient Methods with Online Scaling Part I. Theoretical Foundations
by: Gao, Wenzhi, et al.
Published: (2025)

Foundations of Multivariate Distributional Reinforcement Learning
by: Wiltzer, Harley, et al.
Published: (2024)

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
by: Buyuktahtakin, I. Esra
Published: (2026)

Joint Cooperative and Non-Cooperative Localization in WSNs with Distributed Scaled Proximal ADMM Algorithms
by: Zhu, Qiaojia, et al.
Published: (2025)

Coherent Local Explanations for Mathematical Optimization
by: Otto, Daan, et al.
Published: (2025)

Mathematical Foundations of Modeling ETL Process Chains
by: Maier, Levin, et al.
Published: (2026)

A Mathematical Programming Approach to Optimal Classification Forests
by: Blanco, Víctor, et al.
Published: (2022)

Deep Learning Model Predictive Control for Deep Brain Stimulation in Parkinson's Disease
by: Steffen, Sebastian, et al.
Published: (2025)

Learning Exactly Linearizable Deep Dynamics Models
by: Moriyasu, Ryuta, et al.
Published: (2023)

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
by: Magnino, Lorenzo, et al.
Published: (2024)

Deep Learning for the Multiple Optimal Stopping Problem
by: Laurière, Mathieu, et al.
Published: (2025)

Deep Generative Demand Learning for Newsvendor and Pricing
by: Gong, Shijin, et al.
Published: (2024)

A Mathematical Optimization Approach to Multisphere Support Vector Data Description
by: Blanco, Víctor, et al.
Published: (2025)

FedGiA: An Efficient Hybrid Algorithm for Federated Learning
by: Zhou, Shenglong, et al.
Published: (2022)

Deep Reinforcement Learning: A Convex Optimization Approach
by: Gattami, Ather
Published: (2024)

Structure-Informed Deep Reinforcement Learning for Inventory Management
by: Maggiar, Alvaro, et al.
Published: (2025)

Learning Constrained Optimization with Deep Augmented Lagrangian Methods
by: Kotary, James, et al.
Published: (2024)

Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
by: Tran, Hoang, et al.
Published: (2024)

Training Deep Learning Models with Norm-Constrained LMOs
by: Pethick, Thomas, et al.
Published: (2025)

Sparse Deep Learning Models with the $\ell_1$ Regularization
by: Shen, Lixin, et al.
Published: (2024)

Towards Dynamic Resource Allocation and Client Scheduling in Hierarchical Federated Learning: A Two-Phase Deep Reinforcement Learning Approach
by: Chen, Xiaojing, et al.
Published: (2024)

Model-Free Learning for the Linear Quadratic Regulator over Rate-Limited Channels
by: Ye, Lintao, et al.
Published: (2024)

Exact Matrix Seriation through Mathematical Optimization: Stress and Effectiveness-Based Models
by: Blanco, Víctor, et al.
Published: (2025)

When Deep Learning Meets Polyhedral Theory: A Survey
by: Huchette, Joey, et al.
Published: (2023)

Deep Reinforcement Learning for Dynamic Order Picking in Warehouse Operations
by: Mahmoudinazlou, Sasan, et al.
Published: (2024)

Position: Adopt Constraints Over Fixed Penalties in Deep Learning
by: Ramirez, Juan, et al.
Published: (2025)

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art
by: Peitz, Sebastian, et al.
Published: (2024)

An Efficient On-Policy Deep Learning Framework for Stochastic Optimal Control
by: Hua, Mengjian, et al.
Published: (2024)

Enhancing Accuracy in Deep Learning Using Random Matrix Theory
by: Berlyand, Leonid, et al.
Published: (2023)

DLMMPR:Deep Learning-based Measurement Matrix for Phase Retrieval
by: Liu, Jing, et al.
Published: (2025)

Convergence Analysis for Learning Orthonormal Deep Linear Neural Networks
by: Qin, Zhen, et al.
Published: (2023)

Learning Decentralized Linear Quadratic Regulators with $\sqrt{T}$ Regret
by: Ye, Lintao, et al.
Published: (2022)

Learning Stabilizing Policies via an Unstable Subspace Representation
by: Toso, Leonardo F., et al.
Published: (2025)

EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
by: Yau, Chung-Yiu, et al.
Published: (2026)

Approximation of Solution Operators for High-dimensional PDEs
by: Gaby, Nathan, et al.
Published: (2024)

QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning
by: Fu, Minghan, et al.
Published: (2023)

Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learning
by: Ding, Kuangyu, et al.
Published: (2023)