Independent Sampling

Independent sampling, a crucial aspect of many machine learning and statistical methods, aims to efficiently select data subsets for model training and analysis while minimizing bias and computational cost. Current research focuses on developing adaptive sampling strategies that account for data and system heterogeneity, particularly in federated learning and active learning contexts, often employing algorithms that leverage score-based sampling or distance-based metrics for improved sample selection. These advancements enhance the efficiency and accuracy of various applications, ranging from reinforcement learning and robust model training to object detection and medical image analysis, by optimizing sample utilization and improving model performance.