Active Sampling

Active sampling is a data-efficient strategy that selectively acquires the most informative data points for model training, minimizing the need for exhaustive data collection. Current research focuses on developing sophisticated sampling algorithms, often incorporating machine learning models like Gaussian processes and reinforcement learning, to optimize data selection for various tasks, including model reduction, personalized federated learning, and medical image analysis. This approach is significant because it reduces the cost and time associated with data acquisition, improves model accuracy with limited data, and enhances the feasibility of applying machine learning to resource-constrained applications.