Uniform Sampling

Uniform sampling, the process of selecting data points with equal probability, is a fundamental technique across numerous scientific fields, but its limitations in various applications have spurred significant research into alternative strategies. Current research focuses on developing non-uniform sampling methods tailored to specific problem domains, such as federated learning, reinforcement learning, and optimization algorithms like CMA-ES, often leveraging techniques like low-discrepancy sequences, stratified sampling, or learned sampling policies to improve efficiency and accuracy. These advancements are impacting diverse areas, from improving the performance of machine learning models and accelerating scientific computations to enabling more efficient robotic control and enhancing the quality of medical imaging.