Data Sampling

Data sampling, the process of selecting a subset of data for analysis or model training, aims to optimize resource utilization while maintaining data representativeness and model accuracy. Current research emphasizes developing sophisticated sampling strategies tailored to specific data characteristics and model architectures, including active learning, network flow-based methods for matrix completion, and complexity-guided approaches for program surrogates. These advancements are crucial for addressing challenges like high data dimensionality, imbalanced datasets, and the computational cost of training large models, ultimately improving efficiency and performance in diverse applications such as image processing, traffic prediction, and financial auditing.