Mean Estimation

Mean estimation, the task of accurately determining the average of a dataset, is a fundamental problem across numerous scientific disciplines and applications. Current research emphasizes robust and efficient mean estimation under various constraints, including high dimensionality, data heterogeneity (different distributions and sample sizes across users), privacy concerns (differential privacy), and communication limitations in distributed settings. Active research areas involve developing algorithms that leverage data correlations, handle adversarial attacks or outliers, and achieve optimal trade-offs between accuracy, privacy, and communication costs, often employing techniques like sparsification, randomized projections, and robust statistical methods. These advancements have significant implications for improving the reliability and efficiency of data analysis in diverse fields, from federated learning and statistical inference to robust machine learning and scientific experimentation.