Sampling Strategy

Sampling strategy, the method of selecting data subsets for analysis or model training, is crucial for efficient and accurate results across diverse scientific domains. Current research focuses on optimizing sampling techniques for various applications, including improving the efficiency of machine learning model explanations (e.g., Shapley values), enhancing the accuracy and speed of reduced-order models, and mitigating biases in generative models. Effective sampling strategies are essential for addressing computational limitations, improving model performance, and ensuring fairness and representativeness in data-driven analyses, impacting fields from machine learning and Bayesian inference to remote sensing and robotics.