A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
Fuente:
arXiv
Guardado en:
| Autores principales: | Kumari, Sakshi, M, Shyam Kumar, P, Sushmitha |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
por: Chakrabarti, Kushal, et al.
Publicado: (2022)
por: Chakrabarti, Kushal, et al.
Publicado: (2022)
Faster Adaptive Decentralized Learning Algorithms
por: Huang, Feihu, et al.
Publicado: (2024)
por: Huang, Feihu, et al.
Publicado: (2024)
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
por: Er, Guner Dilsad, et al.
Publicado: (2025)
por: Er, Guner Dilsad, et al.
Publicado: (2025)
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)
Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning
por: Li, Yongqi, et al.
Publicado: (2025)
por: Li, Yongqi, et al.
Publicado: (2025)
AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems
por: Chen, Xi, et al.
Publicado: (2025)
por: Chen, Xi, et al.
Publicado: (2025)
AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm
por: Bojovic, Matia, et al.
Publicado: (2026)
por: Bojovic, Matia, et al.
Publicado: (2026)
Adaptive Delayed-Update Cyclic Algorithm for Variational Inequalities
por: Wei, Yi, et al.
Publicado: (2026)
por: Wei, Yi, et al.
Publicado: (2026)
A Theoretical Analysis of Self-Supervised Learning for Vision Transformers
por: Huang, Yu, et al.
Publicado: (2024)
por: Huang, Yu, et al.
Publicado: (2024)
Towards Robust Learning to Optimize with Theoretical Guarantees
por: Song, Qingyu, et al.
Publicado: (2025)
por: Song, Qingyu, et al.
Publicado: (2025)
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
por: Li, Xiang, et al.
Publicado: (2022)
por: Li, Xiang, et al.
Publicado: (2022)
State Estimation Using Particle Filtering in Adaptive Machine Learning Methods: Integrating Q-Learning and NEAT Algorithms with Noisy Radar Measurements
por: Song, Wonjin, et al.
Publicado: (2025)
por: Song, Wonjin, et al.
Publicado: (2025)
Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization
por: Gong, Xiaochuan, et al.
Publicado: (2025)
por: Gong, Xiaochuan, et al.
Publicado: (2025)
Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms
por: Pokutta, Sebastian
Publicado: (2025)
por: Pokutta, Sebastian
Publicado: (2025)
Control Theoretic Approach to Fine-Tuning and Transfer Learning
por: Bayram, Erkan, et al.
Publicado: (2024)
por: Bayram, Erkan, et al.
Publicado: (2024)
Reevaluating Theoretical Analysis Methods for Optimization in Deep Learning
por: Tran, Hoang, et al.
Publicado: (2024)
por: Tran, Hoang, et al.
Publicado: (2024)
An Efficient Global Optimization Algorithm with Adaptive Estimates of the Local Lipschitz Constants
por: D'Agostino, Danny
Publicado: (2022)
por: D'Agostino, Danny
Publicado: (2022)
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
por: Xie, Xingyu, et al.
Publicado: (2022)
por: Xie, Xingyu, et al.
Publicado: (2022)
Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
por: Liu, Yuxing, et al.
Publicado: (2025)
por: Liu, Yuxing, et al.
Publicado: (2025)
GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms
por: Eleh, Chinedu, et al.
Publicado: (2024)
por: Eleh, Chinedu, et al.
Publicado: (2024)
From Learning to Optimize to Learning Optimization Algorithms
por: Castera, Camille, et al.
Publicado: (2024)
por: Castera, Camille, et al.
Publicado: (2024)
Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation
por: Sucker, Michael, et al.
Publicado: (2024)
por: Sucker, Michael, et al.
Publicado: (2024)
Sporadic Gradient Tracking over Directed Graphs: A Theoretical Perspective on Decentralized Federated Learning
por: Zehtabi, Shahryar, et al.
Publicado: (2026)
por: Zehtabi, Shahryar, et al.
Publicado: (2026)
Locally Adaptive Federated Learning
por: Mukherjee, Sohom, et al.
Publicado: (2023)
por: Mukherjee, Sohom, et al.
Publicado: (2023)
FedGiA: An Efficient Hybrid Algorithm for Federated Learning
por: Zhou, Shenglong, et al.
Publicado: (2022)
por: Zhou, Shenglong, et al.
Publicado: (2022)
Information Theoretically Optimal Sample Complexity of Learning Dynamical Directed Acyclic Graphs
por: Veedu, Mishfad Shaikh, et al.
Publicado: (2023)
por: Veedu, Mishfad Shaikh, et al.
Publicado: (2023)
A New First-Order Meta-Learning Algorithm with Convergence Guarantees
por: Chayti, El Mahdi, et al.
Publicado: (2024)
por: Chayti, El Mahdi, et al.
Publicado: (2024)
Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness
por: Crawshaw, Michael, et al.
Publicado: (2025)
por: Crawshaw, Michael, et al.
Publicado: (2025)
Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
por: Piccialli, Veronica, et al.
Publicado: (2023)
por: Piccialli, Veronica, et al.
Publicado: (2023)
Meta-Learning from Learning Curves for Budget-Limited Algorithm Selection
por: Nguyen, Manh Hung, et al.
Publicado: (2024)
por: Nguyen, Manh Hung, et al.
Publicado: (2024)
ADDQ: Adaptive Distributional Double Q-Learning
por: Döring, Leif, et al.
Publicado: (2025)
por: Döring, Leif, et al.
Publicado: (2025)
A Machine Learning Approach to Two-Stage Adaptive Robust Optimization
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
por: Bertsimas, Dimitris, et al.
Publicado: (2023)
A Novel Hybrid Heuristic-Reinforcement Learning Optimization Approach for a Class of Railcar Shunting Problems
por: Zhao, Ruonan, et al.
Publicado: (2026)
por: Zhao, Ruonan, et al.
Publicado: (2026)
The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
por: Collins-Woodfin, Elizabeth, et al.
Publicado: (2024)
por: Collins-Woodfin, Elizabeth, et al.
Publicado: (2024)
Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
por: Sambharya, Rajiv, et al.
Publicado: (2024)
por: Sambharya, Rajiv, et al.
Publicado: (2024)
A Semidefinite Programming-Based Branch-and-Cut Algorithm for Biclustering
por: Sudoso, Antonio M.
Publicado: (2024)
por: Sudoso, Antonio M.
Publicado: (2024)
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
por: Zhao, Heyang, et al.
Publicado: (2023)
por: Zhao, Heyang, et al.
Publicado: (2023)
UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
por: Belomestny, Denis, et al.
Publicado: (2021)
por: Belomestny, Denis, et al.
Publicado: (2021)
Exact and Heuristic Algorithms for Constrained Biclustering
por: Sudoso, Antonio M.
Publicado: (2025)
por: Sudoso, Antonio M.
Publicado: (2025)
Why Do We Need Warm-up? A Theoretical Perspective
por: Alimisis, Foivos, et al.
Publicado: (2025)
por: Alimisis, Foivos, et al.
Publicado: (2025)
Ejemplares similares
-
A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
por: Chakrabarti, Kushal, et al.
Publicado: (2022) -
Faster Adaptive Decentralized Learning Algorithms
por: Huang, Feihu, et al.
Publicado: (2024) -
A Systems-Theoretic View on the Convergence of Algorithms under Disturbances
por: Er, Guner Dilsad, et al.
Publicado: (2025) -
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) -
Adaptive Moment Estimation Optimization Algorithm Using Projection Gradient for Deep Learning
por: Li, Yongqi, et al.
Publicado: (2025)