activity
20222024
most citedFederated Online Clustering of Bandits

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Stochastic Bandits Robust to Adversarial Attacks

Xuchuang Wang, Jinhang Zuo, Xutong Liu +2

This paper investigates stochastic multi-armed bandit algorithms that are robust to adversarial attacks, where an attacker can first observe the learner's action and {then} alter t…

cs.MM2024

AxiomVision: Accuracy-Guaranteed Adaptive Visual Model Selection for Perspective-Aware Video Analytics

Xiangxiang Dai, Zeyu Zhang, Peng Yang +3

The rapid evolution of multimedia and computer vision technologies requires adaptive visual model deployment strategies to effectively handle diverse tasks and varying environments…

cs.LG2024

Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users

Hantao Yang, Xutong Liu, Zhiyong Wang +4

We study the problem of federated contextual combinatorial cascading bandits, where agents collaborate under the coordination of a central server to provide tailore…

cs.LG20232 cited

Online Clustering of Bandits with Misspecified User Models

Zhiyong Wang, Jize Xie, Xutong Liu +2

The contextual linear bandit is an important online learning problem where given arm features, a learning agent selects an arm at each round to maximize the cumulative rewards in t…

cs.LG20222 cited

Federated Online Clustering of Bandits

Xutong Liu, Haoru Zhao, Tong Yu +2

Contextual multi-armed bandit (MAB) is an important sequential decision-making problem in recommendation systems. A line of works, called the clustering of bandits (CLUB), utilize…