Robust, randomized preconditioning for kernel ridge regression
The episode discusses the paper "Robust, randomized preconditioning for kernel ridge regression," which presents two methods to make solving large systems of equations faster. RPCholesky is used for moderate data sets with a smart column sampling strategy, while KRILL is for massive data sets using a random embedding technique. The discussion covers practical applications and the trade-offs between the two methods.