:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Li, Mingyi, Metel, Michael R., Takeda, Akiko
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Optimization and Control
Accès en ligne:	https://arxiv.org/abs/2506.06990
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Perturbed Iterate SGD for Lipschitz Continuous Loss Functions with Numerical Error and Adaptive Step Sizes
par: Metel, Michael R.
Publié: (2022)

Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
par: Han, Andi, et autres
Publié: (2025)

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
par: Zhang, Liang, et autres
Publié: (2023)

A Framework for Bilevel Optimization on Riemannian Manifolds
par: Han, Andi, et autres
Publié: (2024)

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
par: Sato, Kanji, et autres
Publié: (2022)

Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
par: Li, Yuchen, et autres
Publié: (2024)

Statistically Optimal K-means Clustering via Nonnegative Low-rank Semidefinite Programming
par: Zhuang, Yubo, et autres
Publié: (2023)

A Guaranteed-Stable Neural Network Approach for Optimal Control of Nonlinear Systems
par: Li, Anran, et autres
Publié: (2025)

On the Hardness of Meaningful Local Guarantees in Nonsmooth Nonconvex Optimization
par: Kornowski, Guy, et autres
Publié: (2024)

A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
par: Zhou, Yuhao, et autres
Publié: (2025)

A New First-Order Meta-Learning Algorithm with Convergence Guarantees
par: Chayti, El Mahdi, et autres
Publié: (2024)

Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States
par: Lefringhausen, Robert, et autres
Publié: (2023)

A Data-Driven Real-Time Optimal Power Flow Algorithm Using Local Feedback
par: Liang, Heng, et autres
Publié: (2025)

ROOT-SGD: Sharp Nonasymptotics and Near-Optimal Asymptotics in a Single Algorithm
par: Li, Chris Junchi, et autres
Publié: (2020)

A Sinkhorn-type Algorithm for Constrained Optimal Transport
par: Tang, Xun, et autres
Publié: (2024)

Guarantees of a Preconditioned Subgradient Algorithm for Overparameterized Asymmetric Low-rank Matrix Recovery
par: Giampouras, Paris, et autres
Publié: (2024)

Scalable Approximate Algorithms for Optimal Transport Linear Models
par: Kacprzak, Tomasz, et autres
Publié: (2025)

Optimization over Sparse Support-Preserving Sets: Two-Step Projection with Global Optimality Guarantees
par: de Vazelhes, William, et autres
Publié: (2025)

MAP Estimation with Denoisers: Convergence Rates and Guarantees
par: Pesme, Scott, et autres
Publié: (2025)

Scalable Second-order Riemannian Optimization for $K$-means Clustering
par: Xu, Peng, et autres
Publié: (2025)

Optimal Algorithms for Stochastic Complementary Composite Minimization
par: d'Aspremont, Alexandre, et autres
Publié: (2022)

Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
par: Sucker, Michael, et autres
Publié: (2024)

Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
par: Sinha, Abhishek, et autres
Publié: (2023)

Subspace Optimization for Large Language Models with Convergence Guarantees
par: He, Yutong, et autres
Publié: (2024)

Negative Imaginary Neural ODEs: Learning to Control Mechanical Systems with Stability Guarantees
par: Shi, Kanghong, et autres
Publié: (2025)

Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
par: Li, Michael Lingzhi, et autres
Publié: (2023)

ReLU Networks for Model Predictive Control: Network Complexity and Performance Guarantees
par: Li, Xingchen, et autres
Publié: (2026)

MuonBP: Faster Muon via Block-Periodic Orthogonalization
par: Khaled, Ahmed, et autres
Publié: (2025)

Unraveling the Gradient Descent Dynamics of Transformers
par: Song, Bingqing, et autres
Publié: (2024)

Near-Optimal Algorithms for Group Distributionally Robust Optimization and Beyond
par: Soma, Tasuku, et autres
Publié: (2022)

EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization
par: Yau, Chung-Yiu, et autres
Publié: (2026)

Optimal Local Convergence Rates of Stochastic First-Order Methods under Local $α$-PL
par: Masiha, Saeed, et autres
Publié: (2024)

Neural Contraction Metrics with Formal Guarantees for Discrete-Time Nonlinear Dynamical Systems
par: Li, Haoyu, et autres
Publié: (2025)

Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
par: Chen, Chuyan, et autres
Publié: (2025)

Memory-Efficient LLM Pretraining via Minimalist Optimizer Design
par: Glentis, Athanasios, et autres
Publié: (2025)

A Simple, Optimal and Efficient Algorithm for Online Exp-Concave Optimization
par: Wang, Yi-Han, et autres
Publié: (2025)

A Provably Convergent and Practical Algorithm for Gromov--Wasserstein Optimal Transport
par: Liang, Ling, et autres
Publié: (2026)

Local near-quadratic convergence of Riemannian interior point methods
par: Obara, Mitsuaki, et autres
Publié: (2025)

A Recovery Guarantee for Sparse Neural Networks
par: Fridovich-Keil, Sara, et autres
Publié: (2025)

Towards Robust Learning to Optimize with Theoretical Guarantees
par: Song, Qingyu, et autres
Publié: (2025)