30 citations · 133 across the 29 of their papers we have counts for
5 papers · 1 filter
Efficient Methods for Non-stationary Online Learning
Peng Zhao, Yan-Feng Xie, Lijun Zhang +1
Non-stationary online learning has drawn much attention in recent years. In particular, dynamic regret and adaptive regret are proposed as two principled performance measures for o…
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…
Revisiting Weighted Strategy for Non-stationary Parametric Bandits
Jing Wang, Peng Zhao, Zhi-Hua Zhou
Non-stationary parametric bandits have attracted much attention recently. There are three principled ways to deal with non-stationarity, including sliding-window, weighted, and res…
Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization
Sijia Chen, Yu-Jie Zhang, Wei-Wei Tu +2
Stochastically Extended Adversarial (SEA) model is introduced by Sachs et al. [2022] as an interpolation between stochastic and adversarial online convex optimization. Under the sm…
Adapting to Continuous Covariate Shift via Online Density Ratio Estimation
Yu-Jie Zhang, Zhen-Yu Zhang, Peng Zhao +1
Dealing with distribution shifts is one of the central challenges for modern machine learning. One fundamental situation is the covariate shift, where the input distributions of da…