Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression

arXiv:2403.13300v1 Announce Type: cross
Abstract: Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the convergence rate of Back-fitting in training additive GPs is still an open problem. By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-\mathcal{O}(\frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively. Consequently, Back-fitting requires a minimum …

abstract arxiv bayesian convergence cs.lg feature feature selection gaussian processes gps however kernel popular process processes prove rate regression stat.ml training type via