In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action
is given by the inner product between the action's feature matrix and some fixed, but initially …

Ensemble sampling serves as a practical approximation to Thompson sampling when
maintaining an exact posterior distribution over model parameters is computationally …

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 …

Bandit problems with linear or concave reward have been extensively studied, but relatively
few works have studied bandits with non-concave reward. This work considers a large family …

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 …

Multi-armed bandit (MAB) algorithms are efficient approaches to reduce the opportunity cost
of online experimentation and are used by companies to find the best product from …

This paper is concerned with online targeted advertising on social networks. The main
technical task we address is to estimate the activation probability for user pairs, which …

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 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 …

In the stochastic contextual low-rank matrix bandit problem, the expected reward of an action
is given by the inner product between the action's feature matrix and some fixed, but initially …