Non-Parametric Calibration for Classification. - Jonathan Wenger; Hedvig Kjellström; Rudolph Triebel (AISTATS, 2020)

Probabilistic Linear Solvers for Machine Learning. - Jonathan Wenger; Philipp Hennig (NeurIPS, 2020)

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. - Jonathan Wenger; Geoff Pleiss; Philipp Hennig; John P. Cunningham; Jacob R. Gardner (CoRR, 2021)

ProbNum: Probabilistic Numerics in Python. - Jonathan Wenger; Nicholas Krämer; Marvin Pförtner; Jonathan Schmidt 001; Nathanael Bosch; Nina Effenberger; Johannes Zenn; Alexandra Gessner; Toni Karvonen; François-Xavier Briol; Maren Mahsereci; Philipp Hennig (CoRR, 2021)

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. - Jonathan Wenger; Geoff Pleiss; Philipp Hennig; John P. Cunningham; Jacob R. Gardner (ICML, 2022)

Posterior and Computational Uncertainty in Gaussian Processes. - Jonathan Wenger; Geoff Pleiss; Marvin Pförtner; Philipp Hennig; John P. Cunningham (NeurIPS, 2022)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. - Marvin Pförtner; Ingo Steinwart; Philipp Hennig; Jonathan Wenger (CoRR, 2022)

On the Disconnect Between Theory and Practice of Overparametrized Neural Networks. - Jonathan Wenger; Felix Dangel; Agustinus Kristiadi (CoRR, 2023)

Accelerating Generalized Linear Models by Trading off Computation for Uncertainty. - Lukas Tatzel; Jonathan Wenger; Frank Schneider 001; Philipp Hennig (CoRR, 2023)

Probabilistic Numerical Linear Algebra for Machine Learning - Jonathan Wenger (2023)

Large-Scale Gaussian Processes via Alternating Projection. - Kaiwen Wu; Jonathan Wenger; Haydn Thomas Jones; Geoff Pleiss; Jacob R. Gardner (AISTATS, 2024)

Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference. - Jonathan Wenger; Kaiwen Wu; Philipp Hennig; Jacob R. Gardner; Geoff Pleiss; John P. Cunningham (NeurIPS, 2024)

Computation-Aware Kalman Filtering and Smoothing. - Marvin Pförtner; Jonathan Wenger; Jon Cockayne; Philipp Hennig (AISTATS, 2025)

Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty. - Lukas Tatzel; Jonathan Wenger; Frank Schneider 001; Philipp Hennig (Trans. Mach. Learn. Res., 2025)

Variational Deep Learning via Implicit Regularization. - Jonathan Wenger; Beau Coker; Juraj Marusic; John P. Cunningham (CoRR, 2025)