Fréchet regression has emerged as a promising approach for regression analysis involving non-Euclidean response variables.

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

The theory of Koopman operators allows to deploy non-parametric machine learning algorithms to predict and analyze complex dynamical systems.

For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, also known as Jacobian-free backpropagation, a method as easy as automatic differentiation and as efficient as implicit differentiation for fast algorithms (e.g., superlinear