For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).

Wealso compare CoPrivwith SOTA network optimization methods, including SNL, MetaPruning, etc. CoPriv achieves 9.98 and 3.88 online and total communication reduction with a higher accuracy compared to SNL,respectively.

Hence, the following research questions arise: What is the optimal regret lower bound in contextual MNL bandits?

Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder

Multilevel Monte-Carlo (MLMC) technique and establish its sample and computational complexities.