AdaSwitch: An Adaptive Switching Meta-Algorithm for Learning-Augmented Bounded-Influence Problems
Fuente:
arXiv
Guardado en:
| Autores principales: | Chen, Xi, Chen, Yuze, Zhou, Yuan |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
por: Chen, Xin, et al.
Publicado: (2025)
por: Chen, Xin, et al.
Publicado: (2025)
Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
por: Chen, Xi, et al.
Publicado: (2026)
por: Chen, Xi, et al.
Publicado: (2026)
AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm
por: Bojovic, Matia, et al.
Publicado: (2026)
por: Bojovic, Matia, et al.
Publicado: (2026)
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)
Online Learning for Supervisory Switching Control
por: Sun, Haoyuan, et al.
Publicado: (2026)
por: Sun, Haoyuan, et al.
Publicado: (2026)
GeoAdaLer: Geometric Insights into Adaptive Stochastic Gradient Descent Algorithms
por: Eleh, Chinedu, et al.
Publicado: (2024)
por: Eleh, Chinedu, et al.
Publicado: (2024)
TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization
por: Li, Xiang, et al.
Publicado: (2022)
por: Li, Xiang, et al.
Publicado: (2022)
Learning-Augmented Algorithms for the Bahncard Problem
por: Zhao, Hailiang, et al.
Publicado: (2024)
por: Zhao, Hailiang, et al.
Publicado: (2024)
SOREL: A Stochastic Algorithm for Spectral Risks Minimization
por: Ge, Yuze, et al.
Publicado: (2024)
por: Ge, Yuze, et al.
Publicado: (2024)
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
por: Lau, Tim Tsz-Kit, et al.
Publicado: (2024)
por: Lau, Tim Tsz-Kit, et al.
Publicado: (2024)
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
por: Ostroukhov, Petr, et al.
Publicado: (2024)
por: Ostroukhov, Petr, et al.
Publicado: (2024)
AdaGrad Meets Muon: Adaptive Stepsizes for Orthogonal Updates
por: Zhang, Minxin, et al.
Publicado: (2025)
por: Zhang, Minxin, et al.
Publicado: (2025)
Machine Learning Guided Optimal Transmission Switching to Mitigate Wildfire Ignition Risk
por: Huang, Weimin, et al.
Publicado: (2025)
por: Huang, Weimin, et al.
Publicado: (2025)
AdaFisher: Adaptive Second Order Optimization via Fisher Information
por: Gomes, Damien Martins, et al.
Publicado: (2024)
por: Gomes, Damien Martins, et al.
Publicado: (2024)
A Generic Branch-and-Bound Algorithm for $\ell_0$-Penalized Problems with Supplementary Material
por: Elvira, Clément, et al.
Publicado: (2025)
por: Elvira, Clément, et al.
Publicado: (2025)
Provable Complexity Improvement of AdaGrad over SGD: Upper and Lower Bounds in Stochastic Non-Convex Optimization
por: Jiang, Ruichen, et al.
Publicado: (2024)
por: Jiang, Ruichen, et al.
Publicado: (2024)
Fast Catch-Up, Late Switching: Optimal Batch Size Scheduling via Functional Scaling Laws
por: Wang, Jinbo, et al.
Publicado: (2026)
por: Wang, Jinbo, et al.
Publicado: (2026)
Faster Adaptive Decentralized Learning Algorithms
por: Huang, Feihu, et al.
Publicado: (2024)
por: Huang, Feihu, et al.
Publicado: (2024)
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy
por: Lyu, Jiameng, et al.
Publicado: (2024)
por: Lyu, Jiameng, et al.
Publicado: (2024)
Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming
por: Scavuzzo, Lara, et al.
Publicado: (2024)
por: Scavuzzo, Lara, et al.
Publicado: (2024)
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)
A Re-solving Heuristic for Dynamic Assortment Optimization with Knapsack Constraints
por: Chen, Xi, et al.
Publicado: (2024)
por: Chen, Xi, et al.
Publicado: (2024)
A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems
por: Curtis, Frank E., et al.
Publicado: (2023)
por: Curtis, Frank E., et al.
Publicado: (2023)
A Support-Set Algorithm for Optimization Problems with Nonnegative and Orthogonal Constraints
por: Wang, Lei, et al.
Publicado: (2025)
por: Wang, Lei, et al.
Publicado: (2025)
Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems
por: Zhang, Huaqing, et al.
Publicado: (2024)
por: Zhang, Huaqing, et al.
Publicado: (2024)
On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization
por: Zhou, Dongruo, et al.
Publicado: (2018)
por: Zhou, Dongruo, et al.
Publicado: (2018)
AdaGrad under Anisotropic Smoothness
por: Liu, Yuxing, et al.
Publicado: (2024)
por: Liu, Yuxing, 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)
Can Adaptive Gradient Methods Converge under Heavy-Tailed Noise? A Case Study of AdaGrad
por: Liu, Zijian
Publicado: (2026)
por: Liu, Zijian
Publicado: (2026)
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)
Revisiting Convergence of AdaGrad with Relaxed Assumptions
por: Hong, Yusu, et al.
Publicado: (2024)
por: Hong, Yusu, et al.
Publicado: (2024)
Learning in Inverse Optimization: Incenter Cost, Augmented Suboptimality Loss, and Algorithms
por: Scroccaro, Pedro Zattoni, et al.
Publicado: (2023)
por: Scroccaro, Pedro Zattoni, et al.
Publicado: (2023)
Matching the Statistical Query Lower Bound for $k$-Sparse Parity Problems with Sign Stochastic Gradient Descent
por: Kou, Yiwen, et al.
Publicado: (2024)
por: Kou, Yiwen, et al.
Publicado: (2024)
Learning When to Restart: Nonstationary Newsvendor from Uncensored to Censored Demand
por: Chen, Xin, et al.
Publicado: (2025)
por: Chen, Xin, et al.
Publicado: (2025)
Closing the Gaps: Optimality of Sample Average Approximation for Data-Driven Newsvendor Problems
por: Lyu, Jiameng, et al.
Publicado: (2024)
por: Lyu, Jiameng, et al.
Publicado: (2024)
Integrated Offline and Online Learning to Solve a Large Class of Scheduling Problems
por: Liu, Anbang, et al.
Publicado: (2025)
por: Liu, Anbang, et al.
Publicado: (2025)
Learning-Augmented Algorithms for Online Concave Packing and Convex Covering Problems
por: Grigorescu, Elena, et al.
Publicado: (2024)
por: Grigorescu, Elena, et al.
Publicado: (2024)
Theoretical Analysis on how Learning Rate Warmup Accelerates Convergence
por: Liu, Yuxing, et al.
Publicado: (2025)
por: Liu, Yuxing, et al.
Publicado: (2025)
A Theoretical and Experimental Study of a Novel Adaptive Learning Algorithm
por: Kumari, Sakshi, et al.
Publicado: (2026)
por: Kumari, Sakshi, et al.
Publicado: (2026)
Adaptivity and Universality: Problem-dependent Universal Regret for Online Convex Optimization
por: Zhao, Peng, et al.
Publicado: (2025)
por: Zhao, Peng, et al.
Publicado: (2025)
Ejemplares similares
-
A Minimax-MDP Framework with Future-imposed Conditions for Learning-augmented Problems
por: Chen, Xin, et al.
Publicado: (2025) -
Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment
por: Chen, Xi, et al.
Publicado: (2026) -
AdaGrad-Diff: A New Version of the Adaptive Gradient Algorithm
por: Bojovic, Matia, et al.
Publicado: (2026) -
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
por: Zhao, Heyang, et al.
Publicado: (2023) -
Online Learning for Supervisory Switching Control
por: Sun, Haoyuan, et al.
Publicado: (2026)