Simulators can provide valuable insights for researchers and practitioners who wish to
improve recommender systems, because they allow one to easily tweak the experimental …

We consider the problem of latent bandits with cluster structure where there are multiple
users, each with an associated multi-armed bandit problem. These users are grouped into …

Non-stationary parametric bandits have attracted much attention recently. There are three
principled ways to deal with non-stationarity, including sliding-window, weighted, and restart …

We study the problem of {\em online} low-rank matrix completion with $\mathsf {M} $ users,
$\mathsf {N} $ items and $\mathsf {T} $ rounds. In each round, the algorithm recommends …

We investigate the low rank matrix completion problem in an online setting with ${M} $
users, ${N} $ items, ${T} $ rounds, and an unknown rank-$ r $ reward matrix ${R}\in\mathbb …

We consider the problem of\emph {blocked} collaborative bandits where there are multiple
users, each with an associated multi-armed bandit problem. These users are grouped …

We study the problem of online low-rank matrix completion with $\mathsf {M} $ users,
$\mathsf {N} $ items and $\mathsf {T} $ rounds. In each round, the algorithm recommends …

We consider collaborative multi-user reinforcement learning, where multiple users have the
same state-action space and transition probabilities but different rewards. Under the …

Recommendation systems are crucial in navigating the vast digital market. However, user
data's dynamic and non-stationary nature often hinders their efficacy. Traditional models …

Every year there are 1.9 million deaths world-wide attributed to occupational health and
safety risk factors. To address poor working conditions and fulfill UN's SDG 8," protect labour …