Kernel Quadrature

Kernel quadrature is a numerical integration technique leveraging kernel methods to efficiently approximate integrals, particularly useful when dealing with high-dimensional or complex integrands. Current research focuses on improving its efficiency and scalability through techniques like Nyström approximation, batch processing, and kernel recombination, often applied within frameworks such as Gaussian processes and Bayesian quadrature. These advancements are impacting diverse fields, enabling faster Bayesian inference, more efficient reinforcement learning algorithms, and novel approaches to computer graphics and image processing, such as improved volumetric rendering and texture analysis.