Improved Impossible Tuning and Lipschitz-Adaptive Universal Online Learning with Gradient Variations
Fuente:
arXiv
Guardado en:
| Autores principales: | Takemura, Kei, Matsuno, Ryuta, Sakuma, Keita |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
por: Takemura, Kei, et al.
Publicado: (2026)
por: Takemura, Kei, et al.
Publicado: (2026)
Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
por: Matsuno, Ryuta
Publicado: (2025)
por: Matsuno, Ryuta
Publicado: (2025)
Backward Compatibility in Attributive Explanation and Enhanced Model Training Method
por: Matsuno, Ryuta
Publicado: (2024)
por: Matsuno, Ryuta
Publicado: (2024)
Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
por: Yan, Yu-Hu, et al.
Publicado: (2023)
por: Yan, Yu-Hu, et al.
Publicado: (2023)
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
por: Zhao, Yuheng, et al.
Publicado: (2025)
por: Zhao, Yuheng, et al.
Publicado: (2025)
Gradient-Variation Regret Bounds for Unconstrained Online Learning
por: Zhao, Yuheng, et al.
Publicado: (2026)
por: Zhao, Yuheng, et al.
Publicado: (2026)
Gradient-Variation Online Learning under Generalized Smoothness
por: Xie, Yan-Feng, et al.
Publicado: (2024)
por: Xie, Yan-Feng, et al.
Publicado: (2024)
Step by Step: Adaptive Gradient Descent for Training L-Lipschitz Neural Networks
por: Sung, Kyle, et al.
Publicado: (2025)
por: Sung, Kyle, et al.
Publicado: (2025)
On the Mathematical Impossibility of Safe Universal Approximators
por: Yao, Jasper
Publicado: (2025)
por: Yao, Jasper
Publicado: (2025)
Improving Adaptive Online Learning Using Refined Discretization
por: Zhang, Zhiyu, et al.
Publicado: (2023)
por: Zhang, Zhiyu, et al.
Publicado: (2023)
Adaptive Kernel Selection for Stein Variational Gradient Descent
por: Melcher, Moritz, et al.
Publicado: (2025)
por: Melcher, Moritz, et al.
Publicado: (2025)
MANGO: Meta-Adaptive Network Gradient Optimization for Online Continual Learning
por: Awasthi, Ankita, et al.
Publicado: (2026)
por: Awasthi, Ankita, et al.
Publicado: (2026)
Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variations
por: Yu, Hang, et al.
Publicado: (2026)
por: Yu, Hang, et al.
Publicado: (2026)
Langevin Monte Carlo Beyond Lipschitz Gradient Continuity
por: Benko, Matej, et al.
Publicado: (2024)
por: Benko, Matej, et al.
Publicado: (2024)
Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
por: Yuan, Ganzhao
Publicado: (2026)
por: Yuan, Ganzhao
Publicado: (2026)
Gradient Perturbation: Learning to Perturb Gradients for Adaptive Training
por: Li, Hua
Publicado: (2026)
por: Li, Hua
Publicado: (2026)
Dynamic Momentum Recalibration in Online Gradient Learning
por: Yao, Zhipeng, et al.
Publicado: (2026)
por: Yao, Zhipeng, et al.
Publicado: (2026)
Policy Gradient with Adaptive Entropy Annealing for Continual Fine-Tuning
por: Zhang, Yaqian, et al.
Publicado: (2026)
por: Zhang, Yaqian, et al.
Publicado: (2026)
The Impossibility of Inverse Permutation Learning in Transformer Models
por: Alur, Rohan, et al.
Publicado: (2025)
por: Alur, Rohan, et al.
Publicado: (2025)
GradientSpace: Unsupervised Data Clustering for Improved Instruction Tuning
por: Sridharan, Shrihari, et al.
Publicado: (2025)
por: Sridharan, Shrihari, et al.
Publicado: (2025)
Gradient Descent with Provably Tuned Learning-rate Schedules
por: Sharma, Dravyansh
Publicado: (2025)
por: Sharma, Dravyansh
Publicado: (2025)
A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs
por: Wan, Chang, et al.
Publicado: (2025)
por: Wan, Chang, et al.
Publicado: (2025)
Adaptive Discretization against an Adversary: Lipschitz bandits, Dynamic Pricing, and Auction Tuning
por: Podimata, Chara, et al.
Publicado: (2020)
por: Podimata, Chara, et al.
Publicado: (2020)
Partially Lazy Gradient Descent for Smoothed Online Learning
por: Mhaisen, Naram, et al.
Publicado: (2026)
por: Mhaisen, Naram, et al.
Publicado: (2026)
Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning
por: Bozkurt, Alper Kamil, et al.
Publicado: (2026)
por: Bozkurt, Alper Kamil, et al.
Publicado: (2026)
On the Impossibility of Retrain Equivalence in Machine Unlearning
por: Yu, Jiatong, et al.
Publicado: (2025)
por: Yu, Jiatong, et al.
Publicado: (2025)
Equivariant Frames and the Impossibility of Continuous Canonicalization
por: Dym, Nadav, et al.
Publicado: (2024)
por: Dym, Nadav, et al.
Publicado: (2024)
Adaptive Gradient Clipping for Robust Federated Learning
por: Allouah, Youssef, et al.
Publicado: (2024)
por: Allouah, Youssef, et al.
Publicado: (2024)
Variational Online Mirror Descent for Robust Learning in Schrödinger Bridge
por: Han, Dong-Sig, et al.
Publicado: (2025)
por: Han, Dong-Sig, et al.
Publicado: (2025)
Universal Multiclass Transductive Online Learning
por: Hanneke, Steve, et al.
Publicado: (2026)
por: Hanneke, Steve, et al.
Publicado: (2026)
MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing
por: Kriuk, Boris
Publicado: (2025)
por: Kriuk, Boris
Publicado: (2025)
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)
Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent
por: Banerjee, Sayan, et al.
Publicado: (2024)
por: Banerjee, Sayan, et al.
Publicado: (2024)
Impossibility Theorems for Feature Attribution
por: Bilodeau, Blair, et al.
Publicado: (2022)
por: Bilodeau, Blair, et al.
Publicado: (2022)
Prediction-Powered Semi-Supervised Learning with Online Power Tuning
por: Shoham, Noa, et al.
Publicado: (2025)
por: Shoham, Noa, et al.
Publicado: (2025)
AdaRankGrad: Adaptive Gradient-Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning
por: Refael, Yehonathan, et al.
Publicado: (2024)
por: Refael, Yehonathan, et al.
Publicado: (2024)
Linear Gradient Prediction with Control Variates
por: Ciosek, Kamil, et al.
Publicado: (2025)
por: Ciosek, Kamil, et al.
Publicado: (2025)
Zero-Variance Gradients for Variational Autoencoders
por: Shao, Zilei, et al.
Publicado: (2025)
por: Shao, Zilei, et al.
Publicado: (2025)
Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
por: Yamabe, Shojiro, et al.
Publicado: (2024)
por: Yamabe, Shojiro, et al.
Publicado: (2024)
Wasserstein Gradient Flow over Variational Parameter Space for Variational Inference
por: Nguyen, Dai Hai, et al.
Publicado: (2023)
por: Nguyen, Dai Hai, et al.
Publicado: (2023)
Ejemplares similares
-
Agile Online Model Selection: Resolving Adaptation Lag via Safeguarded Large Learning Rates
por: Takemura, Kei, et al.
Publicado: (2026) -
Source Component Shift Adaptation via Offline Decomposition and Online Mixing Approach
por: Matsuno, Ryuta
Publicado: (2025) -
Backward Compatibility in Attributive Explanation and Enhanced Model Training Method
por: Matsuno, Ryuta
Publicado: (2024) -
Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
por: Yan, Yu-Hu, et al.
Publicado: (2023) -
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
por: Zhao, Yuheng, et al.
Publicado: (2025)