Based Sampling

Based sampling techniques aim to improve the efficiency and effectiveness of data utilization in various machine learning tasks by strategically selecting subsets of data for model training or analysis. Current research focuses on developing sophisticated sampling strategies, often incorporating clustering algorithms to account for data heterogeneity and imbalance, leading to improved model performance and reduced computational costs in applications such as large language model training, federated learning, and sequential recommendation. These advancements are significant because they address limitations of simpler random sampling methods, ultimately leading to more accurate and robust models across diverse domains.