Covariance Estimation

Covariance estimation aims to accurately quantify the relationships between variables within datasets, a crucial step in many statistical and machine learning applications. Current research emphasizes robust and efficient estimation methods, particularly in high-dimensional settings, focusing on techniques like neural networks (e.g., covariance neural networks and their sparse or fair variants), manifold learning, and multifidelity approaches that leverage data from multiple sources. These advancements improve the accuracy and stability of covariance estimates, leading to better performance in diverse fields such as robotics, signal processing, and deep learning, where reliable uncertainty quantification is essential.