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

Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …

The rapid proliferation of decentralized learning systems mandates the need for differentially-
private cooperative learning. In this paper, we study this in context of the contextual linear …

In this paper, we study Federated Bandit, a decentralized Multi-Armed Bandit problem with a
set of N agents, who can only communicate their local data with neighbors described by a …

Daily streamflow forecasting through data-driven approaches is traditionally performed
using a single machine learning algorithm. Existing applications are mostly restricted to …

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 …

We study federated contextual linear bandits, where $ M $ agents cooperate with each other
to solve a global contextual linear bandit problem with the help of a central server. We …

The complexity of threats in the ever-changing environment of modern industry is constantly
increasing. At the same time, traditional security systems fail to detect serious threats of …

-means method using Lloyd heuristic is a traditional clustering method which has played a
key role in multiple downstream tasks of machine learning because of its simplicity …

We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation
tasks that implements the underlying feedback sharing mechanism by estimating user …