… We present a method to approximate Gaussian process regression models for large … that
appear in intermediate steps of the standard Cholesky decomposition, allowing us to modify the …

… the canonical metric of the Gaussian process. This necessitates hav… Gaussian process
covariance function. We present an algorithm toward this by employing an incomplete Cholesky …

… We also discuss the connections between radial basis function and Gaussian process
approximation. … The adaptive algorithm we use is summarized in Algorithm 5 and described here. …

… -upper) of triangular Cholesky decomposition of the inverse … for improvement of adaptive
signal processing performance. … for solving the tasks of adaptive signal processing in various-…

Adaptive learning is … the first adaptive sparse Gaussian process (GP) able to address all
these issues. We first reformulate a variational sparse GP (VSGP) algorithm to make it adaptive …

… adaptive elements or control policies whose number of parameters or features are fixed and
determined a priori. One example of such an adaptive … domain, then adaptive elements may …

… ordering and Pperm (based on adaptive randomized ordering rather than the physically
motivated coarse-to-fine ordering) but without further sparsifying these Cholesky factors. It is of …

… Cholesky factors obtained by observing a Gaussian process … size of the precision matrix
and its Cholesky factor. The key … We will show that operator-adapted wavelets (also known as …

… Here we introduce methods based on Gaussian process nonparametric Bayesian
techniques for estimating these two-dimensional rate maps. These techniques offer a number of …

… Let A = LL and B = MM be the Cholesky decompositions of A and B … Gaussian processes
together with entropic regularization. We discuss some geometric properties of the adapted …