multi-source-learning

This paper proposes MS-WDRO, a multi-source Wasserstein distributionally robust graph learning framework that fuses heterogeneous sources via Wasserstein barycenter and minimizes worst-case risk for robust graph topology inference, outperforming baselines in graph recovery and diagnostic utility.

GRASP proposes a method for multi-source transfer learning that sequentially merges source models into a single target model with constant O(1) memory usage, using gradient-based parameter alignment to avoid negative transfer. Experiments show it outperforms ensemble methods while being much more memory-efficient.