2 citations · 2 across the 2 of their papers we have counts for
8 papers
Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits
Haipeng Luo, Mengxiao Zhang, Peng Zhao +1
We consider the problem of combining and learning over a set of adversarial bandit algorithms with the goal of adaptively tracking the best one on the fly. The CORRAL algorithm of…
No-Regret Learning in Time-Varying Zero-Sum Games
Mengxiao Zhang, Peng Zhao, Haipeng Luo +1
Learning from repeated play in a fixed two-player zero-sum game is a classic problem in game theory and online learning. We consider a variant of this problem where the game payoff…
Storage Fit Learning with Feature Evolvable Streams
Bo-Jian Hou, Yu-Hu Yan, Peng Zhao +1
Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usua…
Dynamic Regret of Convex and Smooth Functions
Peng Zhao, Yu-Jie Zhang, Lijun Zhang +1
We investigate online convex optimization in non-stationary environments and choose the dynamic regret as the performance measure, defined as the difference between cumulative loss…
Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions
Peng Zhao, Lijun Zhang
In this paper, we present an improved analysis for dynamic regret of strongly convex and smooth functions. Specifically, we investigate the Online Multiple Gradient Descent (OMGD)…
An Unbiased Risk Estimator for Learning with Augmented Classes
Yu-Jie Zhang, Peng Zhao, Zhi-Hua Zhou
This paper studies the problem of learning with augmented classes (LAC), where augmented classes unobserved in the training data might emerge in the testing phase. Previous studies…