Convergence AwarE Sampling

Convergence-aware sampling (CAS) techniques aim to improve the efficiency and accuracy of data processing and machine learning by strategically selecting subsets of data points based on their convergence properties. Current research focuses on developing novel sampling methods, such as those incorporating screening mechanisms or adaptive voxel sizes, to optimize for specific tasks like tensor completion, federated reinforcement learning, and point cloud processing. These advancements are significant because they enable more efficient handling of large datasets and improve the performance of various algorithms across diverse applications, including 3D scene understanding and medical image analysis.