We study the stochastic multi-armed bandits problem in the presence of adversarial
corruption. We present a new algorithm for this problem whose regret is nearly optimal …

We consider a stochastic linear bandit problem in which the rewards are not only subject to
random noise, but also adversarial attacks subject to a suitable budget $ C $(ie, an upper …

We consider the problem of optimizing an unknown (typically non-convex) function with a
bounded norm in some Reproducing Kernel Hilbert Space (RKHS), based on noisy bandit …

This work considers a repeated principal-agent bandit game, where the principal can only
interact with her environment through the agent. The principal and the agent have …

This paper studies distributed online learning under Byzantine attacks. The performance of
an online learning algorithm is often characterized by (adversarial) regret, which evaluates …

Contextual bandit algorithms are applied in a wide range of domains, from advertising to
recommender systems, from clinical trials to education. In many of these domains, malicious …

Stochastic linear contextual bandit algorithms have substantial applications in practice, such
as recommender systems, online advertising, clinical trials, etc. Recent works show that …

Recent works have shown that agents facing independent instances of a stochastic K-armed
bandit can collaborate to decrease regret. However, these works assume that each agent …

In many online platforms, customers' decisions are substantially influenced by product
rankings as most customers only examine a few top-ranked products. Concurrently, such …

We study the online learning with feedback graphs framework introduced by Mannor and
Shamir (2011), in which the feedback received by the online learner is specified by a graph …