Announcement_2

I am delighted to share that two of our papers have been accepted for presentation at NeurIPS 2025.

• Fréchet Geodesic Boosting: We introduce FGBoost, a gradient boosting framework designed to intrinsically model complex regression relationships with non-Euclidean outputs in geodesic metric spaces.
• Wasserstein Transfer Learning: We propose a novel transfer learning framework for regression where outputs are probability distributions residing in the Wasserstein space.