137 citations · 138 across the 4 of their papers we have counts for
5 papers
Adaptive Cholesky Gaussian Processes
Simon Bartels, Kristoffer Stensbo-Smidt, Pablo Moreno-Muñoz +3
We present a method to approximate Gaussian process regression models for large datasets by considering only a subset of the data. Our approach is novel in that the size of the sub…
Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition
Simon Bartels, Wouter Boomsma, Jes Frellsen +1
Algorithms involving Gaussian processes or determinantal point processes typically require computing the determinant of a kernel matrix. Frequently, the latter is computed from the…
Conjugate Gradients for Kernel Machines
Simon Bartels, Philipp Hennig
Regularized least-squares (kernel-ridge / Gaussian process) regression is a fundamental algorithm of statistics and machine learning. Because generic algorithms for the exact solut…
Probabilistic Linear Solvers: A Unifying View
Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen +1
Several recent works have developed a new, probabilistic interpretation for numerical algorithms solving linear systems in which the solution is inferred in a Bayesian framework, e…
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets
Aaron Klein, Stefan Falkner, Simon Bartels +2
Bayesian optimization has become a successful tool for hyperparameter optimization of machine learning algorithms, such as support vector machines or deep neural networks. Despite…