Before revision: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, casual discovery, and deep learning.
After revision: Computing the expectation of some kernel function is ubiquitous in machine learning, from the classical theory of support vector machines , to exploiting kernel embeddings of distributions in applications ranging from probabilistic modeling, statistical inference, causal discovery, and deep learning.