Online learning to rank (OLTR) is a sequential decision-making problem where a learning
agent selects an ordered list of items and receives feedback through user clicks. Although …

In decentralized cooperative multi-armed bandits (MAB), each agent observes a distinct
stream of rewards, and seeks to exchange information with others to select a sequence of …

We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first
provide a sufficient and necessary condition for the attackability of CMAB, which depends on …

Most of the existing federated multi-armed bandits (FMAB) designs are based on the
presumption that clients will implement the specified design to collaborate with the server. In …

Adversarial attacks against stochastic multi-armed bandit (MAB) algorithms have been
extensively studied in the literature. In this work, we focus on reward poisoning attacks and …

Standard contextual bandit problem assumes that all the relevant contexts are observed
before the algorithm chooses an arm. This modeling paradigm, while useful, often falls short …

Contextual bandit algorithms have many applicants in a variety of scenarios. In order to
develop trustworthy contextual bandit systems, understanding the impacts of various …

We study a security threat to adversarial multi-armed bandits, in which an attacker perturbs
the loss or reward signal to control the behavior of the victim bandit player. We show that the …

In many real-world reinforcement learning (RL) tasks, the agent who takes the actions often
only has partial observations of the environment. On the other hand, a principal may have a …

We study reward poisoning attacks on Combinatorial Multi-armed Bandits (CMAB). We first
provide a sufficient and necessary condition for the attackability of CMAB, a notion to capture …