Tighter Regret Bounds for Influence Maximization and Other Combinatorial Semi-Bandits with Probabilistically Triggered Arms

We study combinatorial multi-armed bandit with probabilistically triggered arms and semi-bandit feedback (CMAB-T). We resolve a serious issue in the prior CMAB-T studies where the regret bounds contain a possibly exponentially large factor of 1/p, where p is the minimum positive probability that an arm is triggered by any action. We address this issue by introducing a triggering probability modulated (TPM) bounded smoothness condition into the general CMAB-T framework, and show that many applications such as influence maximization bandit and combinatorial cascading bandit satisfy this TPM condition. As a result, we completely remove the factor of 1/p from the regret bounds, achieving significantly better regret bounds for influence maximization and cascading bandits than before. Finally, we provide lower bound results showing that the factor 1/p is unavoidable for general CMAB-T problems, suggesting that the TPM condition is crucial in removing this factor.