Fast sampling of parameterised Gaussian random fields. - Jonas Latz; Marvin Eisenberger; Elisabeth Ullmann (CoRR, 2018)

Multilevel Sequential2 Monte Carlo for Bayesian inverse problems. - Jonas Latz; Iason Papaioannou; Elisabeth Ullmann (J. Comput. Phys., 2018)

Bayesian Parameter Identification in Cahn-Hilliard Models for Biological Growth. - Christian Kahle; Kei Fong Lam; Jonas Latz; Elisabeth Ullmann (SIAM/ASA J. Uncertain. Quantification, 2019)

Exploring and exploiting hierarchies in Bayesian inverse problems (Hierarchische Methoden und Modelle in der Bayes'schen Inversion) - Jonas Latz (2019)

Certified and fast computations with shallow covariance kernels. - Daniel Kressner; Jonas Latz; Stefano Massei; Elisabeth Ullmann (CoRR, 2020)

Classification and image processing with a semi-discrete scheme for fidelity forced Allen-Cahn on graphs. - Jeremy Budd; Yves van Gennip; Jonas Latz (CoRR, 2020)

On the Well-posedness of Bayesian Inverse Problems. - Jonas Latz (SIAM/ASA J. Uncertain. Quantification, 2020)

Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems. - Ionut-Gabriel Farcas; Jonas Latz; Elisabeth Ullmann; Tobias Neckel; Hans-Joachim Bungartz (SIAM J. Sci. Comput., 2020)

Multilevel Sequential Importance Sampling for Rare Event Estimation. - Fabian Wagner; Jonas Latz; Iason Papaioannou; Elisabeth Ullmann (SIAM J. Sci. Comput., 2020)

Analysis of stochastic gradient descent in continuous time. - Jonas Latz (Stat. Comput., 2021)

Generalized parallel tempering on Bayesian inverse problems. - Jonas Latz; Juan P. Madrigal-Cianci; Fabio Nobile; Raúl Tempone (Stat. Comput., 2021)

Error Analysis for Probabilities of Rare Events with Approximate Models. - Fabian Wagner; Jonas Latz; Iason Papaioannou; Elisabeth Ullmann (SIAM J. Numer. Anal., 2021)

Gradient flows and randomised thresholding: sparse inversion and classification. - Jonas Latz (CoRR, 2022)

Losing momentum in continuous-time stochastic optimisation. - Kexin Jin; Jonas Latz; Chenguang Liu; Alessandro Scagliotti (CoRR, 2022)

Can Physics-Informed Neural Networks beat the Finite Element Method? - Tamara G. Grossmann; Urszula Julia Komorowska; Jonas Latz; Carola-Bibiane Schönlieb (CoRR, 2023)

Subsampling in ensemble Kalman inversion. - Matei Hanu; Jonas Latz; Claudia Schillings (CoRR, 2023)

A Continuous-time Stochastic Gradient Descent Method for Continuous Data. - Kexin Jin; Jonas Latz; Chenguang Liu; Carola-Bibiane Schönlieb (J. Mach. Learn. Res., 2023)

Joint Reconstruction-Segmentation on Graphs. - Jeremy Budd; Yves van Gennip; Jonas Latz; Simone Parisotto; Carola-Bibiane Schönlieb (SIAM J. Imaging Sci., 2023)

Bayesian Inverse Problems Are Usually Well-Posed. - Jonas Latz (SIAM Rev., 2023)

Subsampling Error in Stochastic Gradient Langevin Diffusions. - Kexin Jin; Chenguang Liu; Jonas Latz (AISTATS, 2024)

Data-driven approximation of Koopman operators and generators: Convergence rates and error bounds. - Liam Llamazares-Elias; Samir Llamazares-Elías; Jonas Latz; Stefan Klus (CoRR, 2024)

How to beat a Bayesian adversary. - Zihan Ding; Kexin Jin; Jonas Latz; Chenguang Liu (CoRR, 2024)

The random timestep Euler method and its continuous dynamics. - Jonas Latz (CoRR, 2024)

Deep Gaussian Process Priors for Bayesian Image Reconstruction. - Jonas Latz; Aretha L. Teckentrup; Simon Urbainczyk (CoRR, 2024)

Nested Sampling for Uncertainty Quantification and Rare Event Estimation. - Jonas Latz; Doris Schneider; Philipp Wacker (SIAM J. Sci. Comput., 2024)

Adaptive Stepsize Algorithms for Langevin Dynamics. - Alix Leroy; Benedict J. Leimkuhler; Jonas Latz; Desmond J. Higham (SIAM J. Sci. Comput., 2024)

Correction to: analysis of stochastic gradient descent in continuous time. - Jonas Latz (Stat. Comput., 2024)

A Learnable Prior Improves Inverse Tumor Growth Modeling. - Jonas Weidner; Ivan Ezhov; Michal Balcerak; Marie-Christin Metz; Sergey Litvinov; Sebastian Kaltenbach; Leonhard F. Feiner; Laurin Lux; Florian Kofler; Jana Lipková; Jonas Latz; Daniel Rueckert; et al. (IEEE Trans. Medical Imaging, 2025)

STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes. - Simon Urbainczyk; Aretha L. Teckentrup; Jonas Latz (CoRR, 2025)

Lengthscale-informed sparse grids for kernel methods in high dimensions. - Elliot J. Addy; Jonas Latz; Aretha L. Teckentrup (CoRR, 2025)