Kernel Approximation

Kernel approximation techniques aim to efficiently represent complex kernel functions, thereby accelerating computations and enabling the application of kernel methods to large datasets. Current research focuses on developing faster approximation algorithms, such as random Fourier features and localized methods, and extending these techniques to handle diverse kernel types, including asymmetric and indefinite kernels, within various machine learning frameworks like Gaussian processes and support vector machines. These advancements are crucial for scaling kernel methods to real-world applications in areas such as TinyML, reinforcement learning, and modeling complex systems, improving both computational efficiency and model performance.