Yidong Zhou

We propose a novel transfer learning framework for regression where outputs are probability distributions residing in the Wasserstein space.

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