This paper presents a novel federated linear contextual bandits model, where individual
clients face different $ K $-armed stochastic bandits coupled through common global …

A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed,
which is a new bandit paradigm analogous to the federated learning (FL) framework in …

Federated multi-armed bandits (FMAB) is a new bandit paradigm that parallels the federated
learning (FL) framework in supervised learning. It is inspired by practical applications in …

Despite the significant interests and many progresses in decentralized multi-player multi-
armed bandits (MP-MAB) problems in recent years, the regret gap to the natural centralized …

We study multiplayer stochastic multiarmed bandit problems in which the players cannot
communicate, and if two or more players pull the same arm, a collision occurs and the …

We study the multi-agent multi-armed bandit (MAMAB) problem, where agents are factored
into overlapping groups. Each group represents a hyperedge, forming a hypergraph over …

In this paper, we consider the matching of multi-agent multi-armed bandit problem, ie, while
agents prefer arms with higher expected reward, arms also have preferences on agents. In …

Most existing works on federated bandits take it for granted that all clients are altruistic about
sharing their data with the server for the collective good whenever needed. Despite their …

Competitions for shareable and limited resources have long been studied with strategic
agents. In reality, agents often have to learn and maximize the rewards of the resources at …

We introduce the Proportional Payoff Allocation Game (PPA-Game) to model how agents,
akin to content creators on platforms like YouTube and TikTok, compete for divisible …