Guardado en:
| Autor principal: | Nguyen, Quan |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2510.03478 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
por: Ahn, Kwangjun, et al.
Publicado: (2024)
por: Ahn, Kwangjun, et al.
Publicado: (2024)
Adam Converges Without Any Modification On Update Rules
por: Zhang, Yushun, et al.
Publicado: (2026)
por: Zhang, Yushun, et al.
Publicado: (2026)
Provable Adaptivity of Adam under Non-uniform Smoothness
por: Wang, Bohan, et al.
Publicado: (2022)
por: Wang, Bohan, et al.
Publicado: (2022)
HomeAdam: Adam and AdamW Algorithms Sometimes Go Home to Obtain Better Provable Generalization
por: Huang, Feihu, et al.
Publicado: (2026)
por: Huang, Feihu, et al.
Publicado: (2026)
Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
por: Yu, Yaxin, et al.
Publicado: (2026)
por: Yu, Yaxin, et al.
Publicado: (2026)
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
por: Ellis, Benjamin, et al.
Publicado: (2024)
por: Ellis, Benjamin, et al.
Publicado: (2024)
A Theoretical and Empirical Study on the Convergence of Adam with an "Exact" Constant Step Size in Non-Convex Settings
por: Mazumder, Alokendu, et al.
Publicado: (2023)
por: Mazumder, Alokendu, et al.
Publicado: (2023)
Projection-free Online Learning over Strongly Convex Sets
por: Wan, Yuanyu, et al.
Publicado: (2020)
por: Wan, Yuanyu, et al.
Publicado: (2020)
Majorization-minimization for Sparse Nonnegative Matrix Factorization with the $β$-divergence
por: Marmin, Arthur, et al.
Publicado: (2022)
por: Marmin, Arthur, et al.
Publicado: (2022)
Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
por: Yu, Yaxin, et al.
Publicado: (2026)
por: Yu, Yaxin, et al.
Publicado: (2026)
The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective
por: Muehlebach, Michael, et al.
Publicado: (2025)
por: Muehlebach, Michael, et al.
Publicado: (2025)
Level Set Teleportation: An Optimization Perspective
por: Mishkin, Aaron, et al.
Publicado: (2024)
por: Mishkin, Aaron, et al.
Publicado: (2024)
Online Inventory Problems: Beyond the i.i.d. Setting with Online Convex Optimization
por: Hihat, Massil, et al.
Publicado: (2023)
por: Hihat, Massil, et al.
Publicado: (2023)
Efficient First-Order Optimization on the Pareto Set for Multi-Objective Learning under Preference Guidance
por: Chen, Lisha, et al.
Publicado: (2025)
por: Chen, Lisha, et al.
Publicado: (2025)
The Rich and the Simple: On the Implicit Bias of Adam and SGD
por: Vasudeva, Bhavya, et al.
Publicado: (2025)
por: Vasudeva, Bhavya, et al.
Publicado: (2025)
Convergence rates for the Adam optimizer
por: Dereich, Steffen, et al.
Publicado: (2024)
por: Dereich, Steffen, et al.
Publicado: (2024)
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
por: Xiao, Nachuan, et al.
Publicado: (2023)
por: Xiao, Nachuan, et al.
Publicado: (2023)
On Convergence of Adam for Stochastic Optimization under Relaxed Assumptions
por: Hong, Yusu, et al.
Publicado: (2024)
por: Hong, Yusu, et al.
Publicado: (2024)
Efficient Online Large-Margin Classification via Dual Certificates
por: Ho-Nguyen, Nam, et al.
Publicado: (2025)
por: Ho-Nguyen, Nam, et al.
Publicado: (2025)
Adam with model exponential moving average is effective for nonconvex optimization
por: Ahn, Kwangjun, et al.
Publicado: (2024)
por: Ahn, Kwangjun, et al.
Publicado: (2024)
Convergence of Steepest Descent and Adam under Non-Uniform Smoothness
por: Vaswani, Sharan, et al.
Publicado: (2026)
por: Vaswani, Sharan, et al.
Publicado: (2026)
A Semantic-Loss Function Modeling Framework With Task-Oriented Machine Learning Perspectives
por: Nguyen, Ti Ti, et al.
Publicado: (2025)
por: Nguyen, Ti Ti, et al.
Publicado: (2025)
From Distributional Robustness to Robust Statistics: A Confidence Sets Perspective
por: Chan, Gabriel, et al.
Publicado: (2024)
por: Chan, Gabriel, et al.
Publicado: (2024)
On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness
por: Gong, Xiaochuan, et al.
Publicado: (2025)
por: Gong, Xiaochuan, et al.
Publicado: (2025)
Implicit Bias of AdamW: $\ell_\infty$ Norm Constrained Optimization
por: Xie, Shuo, et al.
Publicado: (2024)
por: Xie, Shuo, et al.
Publicado: (2024)
A Comprehensive Framework for Analyzing the Convergence of Adam: Bridging the Gap with SGD
por: Jin, Ruinan, et al.
Publicado: (2024)
por: Jin, Ruinan, et al.
Publicado: (2024)
Muon Outperforms Adam in Tail-End Associative Memory Learning
por: Wang, Shuche, et al.
Publicado: (2025)
por: Wang, Shuche, et al.
Publicado: (2025)
On the $O(\frac{\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\ell_1$ Norm
por: Li, Huan, et al.
Publicado: (2025)
por: Li, Huan, et al.
Publicado: (2025)
Is your batch size the problem? Revisiting the Adam-SGD gap in language modeling
por: Srećković, Teodora, et al.
Publicado: (2025)
por: Srećković, Teodora, et al.
Publicado: (2025)
On the Convergence of Adam under Non-uniform Smoothness: Separability from SGDM and Beyond
por: Wang, Bohan, et al.
Publicado: (2024)
por: Wang, Bohan, et al.
Publicado: (2024)
ODE approximation for the Adam algorithm: General and overparametrized setting
por: Dereich, Steffen, et al.
Publicado: (2025)
por: Dereich, Steffen, et al.
Publicado: (2025)
Learning of Linear Dynamical Systems as a Non-Commutative Polynomial Optimization Problem
por: Zhou, Quan, et al.
Publicado: (2020)
por: Zhou, Quan, et al.
Publicado: (2020)
Clipping Improves Adam-Norm and AdaGrad-Norm when the Noise Is Heavy-Tailed
por: Chezhegov, Savelii, et al.
Publicado: (2024)
por: Chezhegov, Savelii, et al.
Publicado: (2024)
Dynamic Regret via Discounted-to-Dynamic Reduction with Applications to Curved Losses and Adam Optimizer
por: Xie, Yan-Feng, et al.
Publicado: (2026)
por: Xie, Yan-Feng, et al.
Publicado: (2026)
Towards Quantifying the Preconditioning Effect of Adam
por: Das, Rudrajit, et al.
Publicado: (2024)
por: Das, Rudrajit, et al.
Publicado: (2024)
Fully Unconstrained Online Learning
por: Cutkosky, Ashok, et al.
Publicado: (2024)
por: Cutkosky, Ashok, et al.
Publicado: (2024)
On the Implicit Bias of Adam
por: Cattaneo, Matias D., et al.
Publicado: (2023)
por: Cattaneo, Matias D., et al.
Publicado: (2023)
Upper-Linearizability of Online Non-Monotone DR-Submodular Maximization over Down-Closed Convex Sets
por: Lu, Yiyang, et al.
Publicado: (2026)
por: Lu, Yiyang, et al.
Publicado: (2026)
Online Optimization Perspective on First-Order and Zero-Order Decentralized Nonsmooth Nonconvex Stochastic Optimization
por: Sahinoglu, Emre, et al.
Publicado: (2024)
por: Sahinoglu, Emre, et al.
Publicado: (2024)
Safe Online Control-Informed Learning
por: Zhou, Tianyu, et al.
Publicado: (2025)
por: Zhou, Tianyu, et al.
Publicado: (2025)
Ejemplares similares
-
Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise
por: Ahn, Kwangjun, et al.
Publicado: (2024) -
Adam Converges Without Any Modification On Update Rules
por: Zhang, Yushun, et al.
Publicado: (2026) -
Provable Adaptivity of Adam under Non-uniform Smoothness
por: Wang, Bohan, et al.
Publicado: (2022) -
HomeAdam: Adam and AdamW Algorithms Sometimes Go Home to Obtain Better Provable Generalization
por: Huang, Feihu, et al.
Publicado: (2026) -
Adam-HNAG: A Convergent Reformulation of Adam with Accelerated Rate
por: Yu, Yaxin, et al.
Publicado: (2026)