Zachary J. Grey

Geometry-aware conformal prediction on the Grassmannian achieves locally adaptive, manifold-respecting prediction regions that significantly reduce size in heteroscedastic settings while guaranteeing coverage.

Eigenvalues align in Riemannian manifolds, but the dominant eigenspaces reveal surprising differences that could redefine how we analyze shape data.