6 papers
Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variations
Hang Yu, Yu-Hu Yan, Peng Zhao
Gradient-variation online learning has drawn increasing attention due to its deep connections to game theory, optimization, etc. It has been studied extensively in the full-informa…
Adaptivity and Universality: Problem-dependent Universal Regret for Online Convex Optimization
Peng Zhao, Yu-Hu Yan, Hang Yu +1
Universal online learning aims to achieve optimal regret guarantees without requiring prior knowledge of the curvature of online functions. Existing methods have established minima…
Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universality
Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou
In this work, we study offline convex optimization with smooth objectives, where the classical Nesterov's Accelerated Gradient (NAG) method achieves the optimal accelerated converg…
Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness
Yuheng Zhao, Yu-Hu Yan, Kfir Yehuda Levy +1
Smoothness is known to be crucial for acceleration in offline optimization, and for gradient-variation regret minimization in online learning. Interestingly, these two problems are…
A Simple Data Augmentation for Feature Distribution Skewed Federated Learning
Yunlu Yan, Huazhu Fu, Yuexiang Li +4
Federated Learning (FL) facilitates collaborative learning among multiple clients in a distributed manner and ensures the security of privacy. However, its performance inevitably d…
Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach
Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou
In this paper, we propose an online convex optimization approach with two different levels of adaptivity. On a higher level, our approach is agnostic to the unknown types and curva…