Adaptive Sampling

Adaptive sampling optimizes data collection by strategically selecting subsets of a larger dataset, aiming to maximize information gain or model performance while minimizing computational cost. Current research focuses on developing algorithms that dynamically adjust sampling strategies based on factors like data characteristics, model performance, and uncertainty, often employing techniques like importance sampling, active learning, and reinforcement learning within various model architectures, including neural networks and Gaussian processes. This approach is proving valuable across diverse fields, improving efficiency in tasks ranging from deep learning model training and evaluation to robotic exploration and scientific simulations, ultimately leading to more accurate and cost-effective results.