We formulate the problem as a novel variant of a contextual combinatorial multi-armed bandit problem. The context takes the form of a probability distribution over the user's latent topic preference, and rewards are a particular nonlinear function of the selected set and the context. These ...
By leveraging the \emph{coded computing} framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. We propose an online learning policy called \emph{...
A One-Size-Fits-All Solution to Conservative Bandit Problems conservative multi-armed bandits (CMAB), conservative linear bandits (CLB) and conservative contextual combinatorial bandits (CCCB). Different from previous works which consider high probability constraints on the expected reward, we focus on...
Bandit-based Large-Neighborhood Search To solve combinatorial optimization problems, MABWiser is integrated into Adaptive Large Neighborhood Search. The ALNS library enables building metaheuristics for complex optimization problems, whereby MABWiser helps selecting the next best destroy, repair operation (arm...
To address these problems, we propose an algorithm named Dynamic Clustering based Contextual Combinatorial Multi-Armed Bandits (DC3MAB), which consists of three configurable key components. Specifically, a dynamic user clustering strategy enables different users in the same cluster to cooperate in ...
To address this issue, we consider a combinatorial bandit problem where the learner selects S actions from a base set of K actions, and displays the results in S (out of M) different positions. The aim is to maximize the cumulative reward or equivalently minimize the regret with respect to...
By leveraging the coded computing framework in order to tackle failures or stragglers in computation, we formulate this problem using contextual-combinatorial multi-armed bandits (CC-MAB), and aim to maximize the cumulative expected reward. We propose an online learning policy called online coded ...