3 posts tagged with "Implementation"

The Maximum Mean Discrepancy (MMD) is a powerful kernel-based statistic for comparing distributions. In this post, we'll implement the MMD two-sample test from scratch using JAX and explore its applications.

Random Fourier Features (RFF) is a beautiful technique that approximates kernel functions with explicit feature maps, enabling linear-time training while maintaining kernel-like performance. Let's implement it from scratch using JAX!

Kernel Ridge Regression (KRR) is one of the most elegant kernel methods, combining the simplicity of ridge regression with the power of the kernel trick. In this post, we'll implement KRR from scratch using JAX and understand each component.