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Found 4 papers, 3 papers with code
Kernel quadrature with randomly pivoted Cholesky
This paper presents new quadrature rules for functions in a reproducing kernel Hilbert space using nodes drawn by a sampling algorithm known as randomly pivoted Cholesky.
Robust, randomized preconditioning for kernel ridge regression
This paper introduces two randomized preconditioning techniques for robustly solving kernel ridge regression (KRR) problems with a medium to large number of data points ($10^4 \leq N \leq 10^7$).
Efficient error and variance estimation for randomized matrix computations
Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning.
Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations
The randomly pivoted partial Cholesky algorithm (RPCholesky) computes a factorized rank-k approximation of an N x N positive-semidefinite (psd) matrix.
