Low Overlap

Low overlap, referring to situations where data or information from different sources share minimal common ground, presents a significant challenge across diverse scientific domains. Current research focuses on developing robust methods to handle this limitation, employing techniques such as augmented Lagrangian methods for constrained optimization in physics-informed machine learning, entity augmentation for vertically partitioned data in federated learning, and feature splatting for novel view synthesis in computer vision. Addressing low overlap is crucial for advancing fields ranging from robotics (point cloud registration) to natural language processing (multi-narrative analysis), enabling more accurate and efficient data processing and model training even with incomplete or disparate datasets.