Improved Algorithm on Online Clustering of Bandits

Abstract

We generalize the setting of online clustering of bandits by allowing non-uniform distribution over user frequencies. A more efficient algorithm is proposed with simple set structures to represent clusters. We prove a regret bound for the new algorithm which is free of the minimal frequency over users. The experiments on both synthetic and real datasets consistently show the advantage of the new algorithm over existing methods.

References in corpus (2)

• Stochastic Low-Rank Bandits
• Stochastic Rank-1 Bandits

Cited by in corpus (2)

• Kernel Methods for Cooperative Multi-Agent Contextual Bandits
• When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution