Maximum Mean Discrepancy

Maximum Mean Discrepancy (MMD) is a powerful statistical tool used to measure the distance between probability distributions, primarily employed in two-sample testing and generative modeling. Current research focuses on improving MMD's computational efficiency for large datasets, developing novel kernel functions tailored to specific applications (e.g., image fusion, time series analysis), and integrating MMD into various machine learning architectures, including neural networks and generative models like GANs and VAEs. The widespread adoption of MMD stems from its ability to handle complex, high-dimensional data and its versatility across diverse fields, leading to advancements in areas such as anomaly detection, domain adaptation, and robust statistical inference.