Abstract Information-directed sampling (IDS) has recently demonstrated its potential as a
data-efficient reinforcement learning algorithm. However, it is still unclear what is the right …

We consider stochastic graphical bandits, where after pulling an arm, the decision maker
observes rewards of not only the chosen arm but also its neighbors in a feedback graph …

The problem of online learning with graph feedback has been extensively studied in the
literature due to its generality and potential to model various learning tasks. Existing works …

We investigate the extent to which offline demonstration data can improve online learning. It
is natural to expect some improvement, but the question is how, and by how much? We …

We consider stochastic multi-armed bandit problems with graph feedback, where the
decision maker is allowed to observe the neighboring actions of the chosen action. We allow …

We consider the problem of adversarial (non-stochastic) online learning with partial
information feedback, where at each round, a decision maker selects an action from a finite …

Much of the recent literature on bandit learning focuses on algorithms that aim to converge
on an optimal action. One shortcoming is that this orientation does not account for time …

We study the stochastic multi-armed bandit problem with the graph-based feedback
structure introduced by Mannor and Shamir. We analyze the performance of the two most …

We study the adversarial multi-armed bandit problem where the learner is supplied with
partial observations modeled by a\emph {feedback graph} and where shifting to a new …

The bandit problem with graph feedback, proposed in [Mannor and Shamir, NeurIPS 2011],
is modeled by a directed graph $ G=(V, E) $ where $ V $ is the collection of bandit arms, and …