Researcher, Apple
3 papers at NeurIPS 2025
We derive Riemannian metrics from pretrained EBMs to compute data-aware geodesics. Our approach outperforms standard methods across datasets, offering a scalable solution for learning data geometry in high-dimensional spaces.
We propose scaling laws that predict the loss of models when trained on a mixture of source domains.
