Probabilistic Approximate Least-Squares. - Simon Bartels; Philipp Hennig (AISTATS, 2016)

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. - Aaron Klein; Stefan Falkner; Simon Bartels; Philipp Hennig; Frank Hutter (AISTATS, 2017)

Probabilistic linear solvers: a unifying view. - Simon Bartels; Jon Cockayne; Ilse C. F. Ipsen; Philipp Hennig (Stat. Comput., 2019)

Conjugate Gradients for Kernel Machines. - Simon Bartels; Philipp Hennig (J. Mach. Learn. Res., 2020)

Adaptive Cholesky Gaussian Processes. - Simon Bartels; Kristoffer Stensbo-Smidt; Pablo Moreno-Muñoz; Wouter Boomsma; Jes Frellsen; Søren Hauberg (AISTATS, 2023)

Kernel-Matrix Determinant Estimates from stopped Cholesky Decomposition. - Simon Bartels; Wouter Boomsma; Jes Frellsen; Damien Garreau (J. Mach. Learn. Res., 2023)

A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences. - Miguel González Duque; Richard Michael; Simon Bartels; Yevgen Zainchkovskyy; Søren Hauberg; Wouter Boomsma (NeurIPS, 2024)

A Continuous Relaxation for Discrete Bayesian Optimization. - Richard Michael; Simon Bartels; Miguel González Duque; Yevgen Zainchkovskyy; Jes Frellsen; Søren Hauberg; Wouter Boomsma (CoRR, 2024)

A systematic analysis of regression models for protein engineering. - Richard Michael; Jacob Kæstel-Hansen; Peter Mørch Groth; Simon Bartels; Jesper Salomon; Pengfei Tian; Nikos S. Hatzakis; Wouter Boomsma (PLoS Comput. Biol., 2024)