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.

From the learning theory perspective, the learning guarantees of SCO algorithms with known derivatives have been studied in the literature.

Our framework applies to a wide range of SMF-type problems for multi-class classification with auxiliary features.

Despite the remarkable progress, they are vulnerable to deliberate attacks, giving rise to concerns about the reliability and trustworthiness of these models in real-world scenarios.

Lagrangian approach, which involves adding a regularization term to the loss function.