Random Sampling

Random sampling, the process of selecting a subset of data points from a larger population, is a fundamental technique across numerous scientific fields, aiming to efficiently estimate population properties or train models while minimizing computational cost. Current research emphasizes optimizing sampling strategies, such as developing adaptive or persistent sampling methods to improve accuracy and efficiency, particularly in high-dimensional spaces and for complex models like deep neural networks. These advancements are impacting diverse applications, from improving the accuracy and cost-effectiveness of large language model inference to enhancing the speed and interpretability of machine learning in areas like medical image analysis and autonomous driving.