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.

This bound guides the design of an optimal exploration policy attaining minimal sample complexity. However, this lower bound involves solving a hard non-convex optimization problem.