Kernel Mean Embeddings

Kernel mean embeddings (KMEs) provide a powerful way to represent probability distributions as points in a high-dimensional space, enabling comparisons and analysis of distributions using kernel methods. Current research focuses on improving KME's scalability and expressiveness through techniques like neural network integration and Nyström approximation, as well as extending their application to diverse fields such as optimal control, fairness in machine learning, and atomistic simulations. This approach offers advantages in handling complex data, reducing computational burdens, and improving model interpretability, leading to advancements in various scientific domains and practical applications.