:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yu, Hang, Yan, Yu-Hu, Zhao, Peng
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2602.04761
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Adaptivity and Universality: Problem-dependent Universal Regret for Online Convex Optimization
by: Zhao, Peng, et al.
Published: (2025)

Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
by: Zhao, Yuheng, et al.
Published: (2025)

Bandit Convex Optimization with Gradient Prediction Adaptivity
by: Wang, Shuche, et al.
Published: (2026)

Improved Regret for Bandit Convex Optimization with Delayed Feedback
by: Wan, Yuanyu, et al.
Published: (2024)

Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
by: Yan, Yu-Hu, et al.
Published: (2023)

The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization
by: Schliserman, Matan, et al.
Published: (2024)

Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems
by: Partohaghighi, Mohammad, et al.
Published: (2025)

Non-stationary Bandit Convex Optimization: A Comprehensive Study
by: Liu, Xiaoqi, et al.
Published: (2025)

Bandit Convex Optimisation
by: Lattimore, Tor
Published: (2024)

Variational Quantum Optimization with Continuous Bandits
by: Wanner, Marc, et al.
Published: (2025)

Gradient-Variation Online Learning under Generalized Smoothness
by: Xie, Yan-Feng, et al.
Published: (2024)

Mitigating Gradient Overlap in Deep Residual Networks with Gradient Normalization for Improved Non-Convex Optimization
by: Yun, Juyoung
Published: (2024)

Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimization
by: Zhao, Peng, et al.
Published: (2021)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)

Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
by: Chen, Sijia, et al.
Published: (2023)

Gradient-Variation Regret Bounds for Unconstrained Online Learning
by: Zhao, Yuheng, et al.
Published: (2026)

HOME-3: High-Order Momentum Estimator with Third-Power Gradient for Convex and Smooth Nonconvex Optimization
by: Zhang, Wei, et al.
Published: (2025)

Optimal High-Probability Regret for Online Convex Optimization with Two-Point Bandit Feedback
by: Ye, Haishan
Published: (2026)

Non-stationary Delayed Online Convex Optimization: From Full-information to Bandit Setting
by: Wan, Yuanyu, et al.
Published: (2023)

Shuffling Momentum Gradient Algorithm for Convex Optimization
by: Tran, Trang H., et al.
Published: (2024)

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification
by: Liu, Haolin, et al.
Published: (2024)

Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update
by: Zhang, Yu-Jie, et al.
Published: (2025)

CR-LSO: Convex Neural Architecture Optimization in the Latent Space of Graph Variational Autoencoder with Input Convex Neural Networks
by: Rao, Xuan, et al.
Published: (2022)

Improved Algorithm for Adversarial Linear Mixture MDPs with Bandit Feedback and Unknown Transition
by: Li, Long-Fei, et al.
Published: (2024)

Stochastic Gradient Succeeds for Bandits
by: Mei, Jincheng, et al.
Published: (2024)

Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update
by: Wang, Jing, et al.
Published: (2025)

Small Gradient Norm Regret for Online Convex Optimization
by: Gao, Wenzhi, et al.
Published: (2026)

Stochastic Non-Smooth Convex Optimization with Unbounded Gradients
by: Kovalev, Dmitry
Published: (2026)

The Sample Complexity of Gradient Descent in Stochastic Convex Optimization
by: Livni, Roi
Published: (2024)

Online Newton Method for Bandit Convex Optimisation
by: Fokkema, Hidde, et al.
Published: (2024)

Adaptive Proximal Gradient Method for Convex Optimization
by: Malitsky, Yura, et al.
Published: (2023)

An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
by: van Erven, Tim, et al.
Published: (2025)

How to Make the Gradients Small Privately: Improved Rates for Differentially Private Non-Convex Optimization
by: Lowy, Andrew, et al.
Published: (2024)

Fréchet Cumulative Covariance Net for Deep Nonlinear Sufficient Dimension Reduction with Random Objects
by: Yuan, Hang, et al.
Published: (2025)

All ERMs Can Fail in Stochastic Convex Optimization Lower Bounds in Linear Dimension
by: Burla, Tal, et al.
Published: (2026)

Improved Bound for Robust Causal Bandits with Linear Models
by: Yan, Zirui, et al.
Published: (2024)

Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
by: Zhang, Liang, et al.
Published: (2023)

An Accelerated Gradient Method for Convex Smooth Simple Bilevel Optimization
by: Cao, Jincheng, et al.
Published: (2024)

Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders
by: Mancisidor, Rogelio A, et al.
Published: (2025)

AYLA: Amplifying Gradient Sensitivity via Loss Transformation in Non-Convex Optimization
by: Keslaki, Ben
Published: (2025)