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

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a
variety of ad hoc border control protocols to allow for non-essential travel while safeguarding …

Firms using online advertising regularly run experiments with multiple versions of their ads
since they are uncertain about which ones are most effective. During a campaign, firms try to …

In this paper, we study the multi-armed bandit problem in the batched setting where the
employed policy must split data into a small number of batches. While the minimax regret for …

We consider the general (stochastic) contextual bandit problem under the realizability
assumption, that is, the expected reward, as a function of contexts and actions, belongs to a …

As bandit algorithms are increasingly utilized in scientific studies and industrial applications,
there is an associated increasing need for reliable inference methods based on the resulting …

We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is,
algorithms that change its exploration policy as infrequently as possible during regret …

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 …

We study a variant of the stochastic $ K $-armed bandit problem, which we call" bandits with
delayed, aggregated anonymous feedback”. In this problem, when the player pulls an arm, a …

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed
bandit problem, where several players pull arms simultaneously and collisions occur if one …