We propose a new class of vector transport based gradient descent enabling silver stepsize acceleration on Riemannian manifolds, yielding provable accelerated gradient methods for potential functional optimization in Wasserstein space.

We propose FGBoost, a gradient boosting framework designed to intrinsically model complex regression relationships with non-Euclidean outputs in geodesic metric spaces.