Stochastic Gradient Descent for Gaussian Processes Done Right

arXiv:2310.20581v2 Announce Type: replace
Abstract: As is well known, both sampling from the posterior and computing the mean of the posterior in Gaussian process regression reduces to solving a large linear system of equations. We study the use of stochastic gradient descent for solving this linear system, and show that when \emph{done right} -- by which we mean using specific insights from the optimisation and kernel communities -- stochastic gradient descent is highly effective. To that end, we introduce a …

abstract arxiv computing cs.lg gaussian processes gradient linear mean posterior process processes regression sampling show stat.ml stochastic study type