Lixing Chen
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
In this paper, we study the stochastic contextual combinatorial multi-armed bandit (CC-MAB) framework that is tailored for volatile arms and submodular reward functions. CC-MAB inherits properties from both contextual bandit and combinatorial bandit: it aims to select a set of arms in each round based on the side information (a.k.a.
Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
Neural Information Processing Systems http://nips.cc/