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)

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)

Sample Path Regularity of Gaussian Processes from the Covariance Kernel. - Nathaël Da Costa; Marvin Pförtner; Lancelot Da Costa; Philipp Hennig (CoRR, 2023)

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning. - Tristan Cinquin; Marvin Pförtner; Vincent Fortuin; Philipp Hennig; Robert Bamler (NeurIPS, 2024)

Reparameterization invariance in approximate Bayesian inference. - Hrittik Roy; Marco Miani; Carl Henrik Ek; Philipp Hennig; Marvin Pförtner; Lukas Tatzel; Søren Hauberg (NeurIPS, 2024)

Scaling up Probabilistic PDE Simulators with Structured Volumetric Information. - Tim Weiland; Marvin Pförtner; Philipp Hennig (CoRR, 2024)

Linearization Turns Neural Operators into Function-Valued Gaussian Processes. - Emilia Magnani; Marvin Pförtner; Tobias Weber; Philipp Hennig (CoRR, 2024)

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

Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields. - Tim Weiland; Marvin Pförtner; Philipp Hennig (AISTATS, 2025)

laplax - Laplace Approximations with JAX. - Tobias Weber; Bálint Mucsányi; Lenard Rommel; Thomas Christie; Lars Kasüschke; Marvin Pförtner; Philipp Hennig (CoRR, 2025)