Reviews: Interactive Submodular Bandit

Pros: - The paper combines submodular function maximization with contextual bandit, and propose the problem of interactive submodular bandit formulation and SM-UCB solution. It includes several existing studies as special cases. Cons: - The discussion on the key RKHS condition is not enough, and its applicability for actual applications is unclear. More importantly, there is no discussion about constant B for the RKHS norm for various applications, and also the maximum information gain \gamma_t, but both of them are needed in the algorithm. The additional technical contribution is small.