Sample Allocation

Sample allocation, the process of selecting representative subsets from larger datasets, is crucial for efficient and unbiased data analysis across diverse fields. Current research focuses on optimizing sample selection through techniques like machine learning classifiers (e.g., Naive Bayes) to target high-risk samples, uncertainty-based methods to minimize model loss in deep learning, and stratified sampling to reduce variance in federated learning. These advancements aim to improve the accuracy and efficiency of data analysis while mitigating biases, particularly in large or heterogeneous datasets, impacting fields ranging from auditing to robotics and AI.