2 papers
cs.LG2026
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
Zhichen Zeng, Ruizhong Qiu, Wenxuan Bao +6
Graph neural networks, despite their impressive performance, are highly vulnerable to distribution shifts on graphs. Existing graph domain adaptation (graph DA) methods often impli…
cs.LG2024
F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data
Zexing Xu, Linjun Zhang, Sitan Yang +4
Demand prediction is a crucial task for e-commerce and physical retail businesses, especially during high-stake sales events. However, the limited availability of historical data f…