Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of
Things (IoT) applications and services, spanning from recommendation systems and speech …

We consider a multi-armed bandit game where N players compete for K arms for T turns.
Each player has different expected rewards for the arms, and the instantaneous rewards are …

Motivated by emerging need of learning algorithms for large scale networked and
decentralized systems, we introduce a distributed version of the classical stochastic Multi …

We study a decentralized cooperative stochastic multi-armed bandit problem with K arms on
a network of N agents. In our model, the reward distribution of each arm is the same for each …

Contextual bandit algorithms are commonly used in recommender systems, where content
popularity can change rapidly. These algorithms continuously learn latent mappings …

We study the problem of regret minimization for distributed bandits learning, in which $ M $
agents work collaboratively to minimize their total regret under the coordination of a central …

In many learning settings, active/adaptive querying is possible, but the number of rounds of
adaptivity is limited. We study the relationship between query complexity and adaptivity in …

Motivated by practical needs such as large-scale learning, we study the impact of adaptivity
constraints to linear contextual bandits, a central problem in online learning and decision …

This paper introduces a general multi-agent bandit model in which each agent is facing a
finite set of arms and may communicate with other agents through a central controller in …

We design decentralized algorithms for regret minimization in the two sided matching market
with one-sided bandit feedback that significantly improves upon the prior works (Liu et al …