Explicitmapsalsoallow analytical path planning and thus lead to significantly better exploration and sample complexity.

Commonly used classification algorithms in machine learning, such as support vector machines, minimize a convex surrogate loss on training examples. In practice, these algorithms are surprisingly robusttoerrors inthe training data.

Kernel density estimation (KDE) is integral to a range of generative and discriminative tasks in machine learning.