Full Professor, Northeastern University
2 papers at NeurIPS 2025
This paper proposes a data-free dual orthogonal projection framework to perform continual model merging.
We propose a self-supervised coloring learning framework for heterophilic graph representation, which effectively captures both local and global structures without relying on delicate augmentation strategies.
