Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy. - Mark Bangert; Philipp Hennig; Uwe Oelfke (ICMLA, 2010)

Coherent Inference on Optimal Play in Game Trees. - Philipp Hennig; David H. Stern; Thore Graepel (AISTATS, 2010)

Optimal Reinforcement Learning for Gaussian Systems. - Philipp Hennig (NIPS, 2011)

Approximate inference in graphical models. - Philipp Hennig (2011)

Learning tracking control with forward models. - Botond Bocsi; Philipp Hennig; Lehel Csató; Jan Peters 001 (ICRA, 2012)

Entropy Search for Information-Efficient Global Optimization. - Philipp Hennig; Christian J. Schuler (J. Mach. Learn. Res., 2012)

Kernel Topic Models. - Philipp Hennig; David H. Stern; Ralf Herbrich; Thore Graepel (AISTATS, 2012)

Nonparametric dynamics estimation for time periodic systems. - Edgar D. Klenske; Melanie Nicole Zeilinger; Bernhard Schölkopf; Philipp Hennig (Allerton, 2013)

Fast Probabilistic Optimization from Noisy Gradients. - Philipp Hennig (ICML (1), 2013)

The Randomized Dependence Coefficient. - David López-Paz; Philipp Hennig; Bernhard Schölkopf (NIPS, 2013)

Probabilistic Numerical Analysis in Riemannian Statistics. - Philipp Hennig; Søren Hauberg (CoRR, 2013)

Quasi-Newton methods: a new direction. - Philipp Hennig; Martin Kiefel (J. Mach. Learn. Res., 2013)

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics. - Philipp Hennig; Søren Hauberg (AISTATS, 2014)

Probabilistic Progress Bars. - Martin Kiefel; Christian J. Schuler; Philipp Hennig (GCPR, 2014)

Efficient Bayesian local model learning for control. - Franziska Meier; Philipp Hennig; Stefan Schaal (IROS, 2014)

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers. - Michael Schober; Niklas Kasenburg; Aasa Feragen; Philipp Hennig; Søren Hauberg (MICCAI (3), 2014)

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature. - Tom Gunter; Michael A. Osborne; Roman Garnett; Philipp Hennig; Stephen J. Roberts (NIPS, 2014)

Incremental Local Gaussian Regression. - Franziska Meier; Philipp Hennig; Stefan Schaal (NIPS, 2014)

Probabilistic ODE Solvers with Runge-Kutta Means. - Michael Schober; David Duvenaud; Philipp Hennig (NIPS, 2014)

Active Learning of Linear Embeddings for Gaussian Processes. - Roman Garnett; Michael A. Osborne; Philipp Hennig (UAI, 2014)

Local Gaussian Regression. - Franziska Meier; Philipp Hennig; Stefan Schaal (CoRR, 2014)

Inference of Cause and Effect with Unsupervised Inverse Regression. - Eleni Sgouritsa; Dominik Janzing; Philipp Hennig; Bernhard Schölkopf (AISTATS, 2015)

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography. - Søren Hauberg; Michael Schober; Matthew G. Liptrot; Philipp Hennig; Aasa Feragen (MICCAI (1), 2015)

Probabilistic Numerics and Uncertainty in Computations. - Philipp Hennig; Michael A. Osborne; Mark A. Girolami (CoRR, 2015)

Probabilistic Interpretation of Linear Solvers. - Philipp Hennig (SIAM J. Optim., 2015)

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

Batch Bayesian Optimization via Local Penalization. - Javier González 002; Zhenwen Dai; Philipp Hennig; Neil D. Lawrence (AISTATS, 2016)

Approximate dual control maintaining the value of information with an application to building control. - Edgar D. Klenske; Philipp Hennig; Bernhard Schölkopf; Melanie Nicole Zeilinger (ECC, 2016)

Automatic LQR tuning based on Gaussian process global optimization. - Alonso Marco; Philipp Hennig; Jeannette Bohg; Stefan Schaal; Sebastian Trimpe (ICRA, 2016)

Active Uncertainty Calibration in Bayesian ODE Solvers. - Hans Kersting; Philipp Hennig (UAI, 2016)

Exact Sampling from Determinantal Point Processes. - Philipp Hennig; Roman Garnett (CoRR, 2016)

New Directions for Learning with Kernels and Gaussian Processes (Dagstuhl Seminar 16481). - Arthur Gretton; Philipp Hennig; Carl Edward Rasmussen; Bernhard Schölkopf (Dagstuhl Reports, 2016)

Dual Control for Approximate Bayesian Reinforcement Learning. - Edgar D. Klenske; Philipp Hennig (J. Mach. Learn. Res., 2016)

Gaussian Process-Based Predictive Control for Periodic Error Correction. - Edgar D. Klenske; Melanie Nicole Zeilinger; Bernhard Schölkopf; Philipp Hennig (IEEE Trans. Control. Syst. Technol., 2016)

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

On the design of LQR kernels for efficient controller learning. - Alonso Marco; Philipp Hennig; Stefan Schaal; Sebastian Trimpe (CDC, 2017)

Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization. - Alonso Marco; Felix Berkenkamp; Philipp Hennig; Angela P. Schoellig; Andreas Krause 001; Stefan Schaal; Sebastian Trimpe (ICRA, 2017)

Coupling Adaptive Batch Sizes with Learning Rates. - Lukas Balles; Javier Romero 002; Philipp Hennig (UAI, 2017)

Follow the Signs for Robust Stochastic Optimization. - Lukas Balles; Philipp Hennig (CoRR, 2017)

Early Stopping without a Validation Set. - Maren Mahsereci; Lukas Balles; Christoph Lassner; Philipp Hennig (CoRR, 2017)

Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning. - Filip de Roos; Philipp Hennig (CoRR, 2017)

Probabilistic Active Learning of Functions in Structural Causal Models. - Paul K. Rubenstein; Ilya O. Tolstikhin; Philipp Hennig; Bernhard Schölkopf (CoRR, 2017)

Bayesian Filtering for ODEs with Bounded Derivatives. - Emilia Magnani; Hans Kersting; Michael Schober; Philipp Hennig (CoRR, 2017)

Probabilistic Line Searches for Stochastic Optimization. - Maren Mahsereci; Philipp Hennig (J. Mach. Learn. Res., 2017)

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. - Lukas Balles; Philipp Hennig (ICML, 2018)

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2018)

Fast and Robust Shortest Paths on Manifolds Learned from Data. - Georgios Arvanitidis; Søren Hauberg; Philipp Hennig; Michael Schober (AISTATS, 2019)

Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. - Filip de Roos; Philipp Hennig (AISTATS, 2019)

DeepOBS: A Deep Learning Optimizer Benchmark Suite. - Frank Schneider 001; Lukas Balles; Philipp Hennig (ICLR (Poster), 2019)

Convergence Guarantees for Adaptive Bayesian Quadrature Methods. - Motonobu Kanagawa; Philipp Hennig (NeurIPS, 2019)

Limitations of the empirical Fisher approximation for natural gradient descent. - Frederik Kunstner; Philipp Hennig; Lukas Balles (NeurIPS, 2019)

A Modular Approach to Block-diagonal Hessian Approximations for Second-order Optimization Methods. - Felix Dangel; Philipp Hennig (CoRR, 2019)

Limitations of the Empirical Fisher Approximation. - Frederik Kunstner; Lukas Balles; Philipp Hennig (CoRR, 2019)

Uncertainty Estimates for Ordinal Embeddings. - Michael Lohaus; Philipp Hennig; Ulrike von Luxburg (CoRR, 2019)

Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties. - Alonso Marco; Dominik Baumann; Philipp Hennig; Sebastian Trimpe (CoRR, 2019)

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

A probabilistic model for the numerical solution of initial value problems. - Michael Schober; Simo Särkkä; Philipp Hennig (Stat. Comput., 2019)

Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective. - Filip Tronarp; Hans Kersting; Simo Särkkä; Philipp Hennig (Stat. Comput., 2019)

Modular Block-diagonal Curvature Approximations for Feedforward Architectures. - Felix Dangel; Stefan Harmeling; Philipp Hennig (AISTATS, 2020)

Integrals over Gaussians under Linear Domain Constraints. - Alexandra Gessner; Oindrila Kanjilal; Philipp Hennig (AISTATS, 2020)

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. - Ricky T. Q. Chen; Dami Choi; Lukas Balles; David Duvenaud; Philipp Hennig (ICBINB@NeurIPS, 2020)

BackPACK: Packing more into Backprop. - Felix Dangel; Frederik Kunstner; Philipp Hennig (ICLR, 2020)

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. - Hans Kersting; Nicholas Krämer; Martin Schiegg; Christian Daniel; Michael Tiemann 001; Philipp Hennig (ICML, 2020)

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (ICML, 2020)

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

When are Neural ODE Solutions Proper ODEs? - Katharina Ott; Prateek Katiyar; Philipp Hennig; Michael Tiemann 001 (CoRR, 2020)

Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (CoRR, 2020)

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

Convergence rates of Gaussian ODE filters. - Hans Kersting; Timothy John Sullivan; Philipp Hennig (Stat. Comput., 2020)

Calibrated Adaptive Probabilistic ODE Solvers. - Nathanael Bosch; Philipp Hennig; Filip Tronarp (AISTATS, 2021)

ResNet After All: Neural ODEs and Their Numerical Solution. - Katharina Ott; Prateek Katiyar; Philipp Hennig; Michael Tiemann 001 (ICLR, 2021)

Bayesian Quadrature on Riemannian Data Manifolds. - Christian Fröhlich; Alexandra Gessner; Philipp Hennig; Bernhard Schölkopf; Georgios Arvanitidis (ICML, 2021)

High-Dimensional Gaussian Process Inference with Derivatives. - Filip de Roos; Alexandra Gessner; Philipp Hennig (ICML, 2021)

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. - Robin M. Schmidt; Frank Schneider 001; Philipp Hennig (ICML, 2021)

Laplace Redux - Effortless Bayesian Deep Learning. - Erik A. Daxberger; Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Matthias Bauer 001; Philipp Hennig (NeurIPS, 2021)

Linear-Time Probabilistic Solution of Boundary Value Problems. - Nicholas Krämer; Philipp Hennig (NeurIPS, 2021)

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (NeurIPS, 2021)

A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. - Jonathan Schmidt 001; Nicholas Krämer; Philipp Hennig (NeurIPS, 2021)

Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. - Frank Schneider 001; Felix Dangel; Philipp Hennig (NeurIPS, 2021)

Probabilistic DAG search. - Julia Grosse; Cheng Zhang; Philipp Hennig (UAI, 2021)

Learnable uncertainty under Laplace approximations. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (UAI, 2021)

A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization. - Filip de Roos; Carl Jidling; Adrian Wills; Thomas B. Schön; Philipp Hennig (CoRR, 2021)

Laplace Matching for fast Approximate Inference in Generalized Linear Models. - Marius Hobbhahn; Philipp Hennig (CoRR, 2021)

Informed Equation Learning. - Matthias Werner; Andrej Junginger; Philipp Hennig; Georg Martius (CoRR, 2021)

Linear-Time Probabilistic Solutions of Boundary Value Problems. - Nicholas Krämer; Philipp Hennig (CoRR, 2021)

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

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. - Runa Eschenhagen; Erik A. Daxberger; Philipp Hennig; Agustinus Kristiadi (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)

Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432). - Philipp Hennig; Ilse C. F. Ipsen; Maren Mahsereci; Tim Sullivan 001 (Dagstuhl Reports, 2021)

Robot Learning With Crash Constraints. - Alonso Marco; Dominik Baumann; Majid Khadiv; Philipp Hennig; Ludovic Righetti; Sebastian Trimpe (IEEE Robotics Autom. Lett., 2021)

Bayesian ODE solvers: the maximum a posteriori estimate. - Filip Tronarp; Simo Särkkä; Philipp Hennig (Stat. Comput., 2021)

Pick-and-Mix Information Operators for Probabilistic ODE Solvers. - Nathanael Bosch; Filip Tronarp; Philipp Hennig (AISTATS, 2022)

Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. - Nicholas Krämer; Jonathan Schmidt 001; Philipp Hennig (AISTATS, 2022)

Being a Bit Frequentist Improves Bayesian Neural Networks. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (AISTATS, 2022)

Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. - Luca Rendsburg; Agustinus Kristiadi; Philipp Hennig; Ulrike von Luxburg (AISTATS, 2022)

Uncertainty in equation learning. - Matthias Werner; Andrej Junginger; Philipp Hennig; Georg Martius (GECCO Companion, 2022)

Probabilistic ODE Solutions in Millions of Dimensions. - Nicholas Krämer; Nathanael Bosch; Jonathan Schmidt 001; Philipp Hennig (ICML, 2022)

Fenrir: Physics-Enhanced Regression for Initial Value Problems. - Filip Tronarp; Nathanael Bosch; Philipp Hennig (ICML, 2022)

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

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. - Agustinus Kristiadi; Runa Eschenhagen; Philipp Hennig (NeurIPS, 2022)

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

Wasserstein t-SNE. - Fynn Bachmann; Philipp Hennig; Dmitry Kobak (ECML/PKDD (1), 2022)

Fast predictive uncertainty for classification with Bayesian deep networks. - Marius Hobbhahn; Agustinus Kristiadi; Philipp Hennig (UAI, 2022)

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. - Emilia Magnani; Nicholas Krämer; Runa Eschenhagen; Lorenzo Rosasco; Philipp Hennig (CoRR, 2022)

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

Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. - Jonathan Oesterle; Nicholas Krämer; Philipp Hennig; Philipp Berens (J. Comput. Neurosci., 2022)

Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. - Jonathan Oesterle; Nicholas Krämer; Philipp Hennig; Philipp Berens (J. Comput. Neurosci., 2022)

Probabilistic Exponential Integrators. - Nathanael Bosch; Philipp Hennig; Filip Tronarp (NeurIPS, 2023)

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. - Runa Eschenhagen; Alexander Immer; Richard E. Turner; Frank Schneider 001; Philipp Hennig (NeurIPS, 2023)

The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. - Agustinus Kristiadi; Felix Dangel; Philipp Hennig (NeurIPS, 2023)

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. - Jonathan Schmidt 001; Philipp Hennig; Jörg Nick; Filip Tronarp (NeurIPS, 2023)

Baysian numerical integration with neural networks. - Katharina Ott; Michael Tiemann 001; Philipp Hennig; François-Xavier Briol (UAI, 2023)

Bayesian Numerical Integration with Neural Networks. - Katharina Ott; Michael Tiemann 001; Philipp Hennig; François-Xavier Briol (CoRR, 2023)

Uncertainty and Structure in Neural Ordinary Differential Equations. - Katharina Ott; Michael Tiemann 001; Philipp Hennig (CoRR, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 2023)

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

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

ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure. - Felix Dangel; Lukas Tatzel; Philipp Hennig (Trans. Mach. Learn. Res., 2023)

Optimistic Optimization of Gaussian Process Samples. - Julia Grosse; Cheng Zhang; Philipp Hennig (Trans. Mach. Learn. Res., 2023)

A Greedy Approximation for k-Determinantal Point Processes. - Julia Grosse; Rahel Fischer; Roman Garnett; Philipp Hennig (AISTATS, 2024)

Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations. - Jonas Beck; Nathanael Bosch; Michael Deistler; Kyra L. Kadhim; Jakob H. Macke; Philipp Hennig; Philipp Berens (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Probabilistic ODE solvers for integration error-aware numerical optimal control. - Amon Lahr; Filip Tronarp; Nathanael Bosch; Jonathan Schmidt 001; Philipp Hennig; Melanie N. Zeilinger (L4DC, 2024)

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)

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)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Flexible inference in heterogeneous and attributed multilayer networks. - Martina Contisciani; Marius Hobbhahn; Eleanor A. Power; Philipp Hennig; Caterina De Bacco (CoRR, 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)

Uncertainty-Guided Optimization on Large Language Model Search Trees. - Julia Grosse; Ruotian Wu; Ahmad Rashid; Philipp Hennig; Pascal Poupart; Agustinus Kristiadi (CoRR, 2024)

Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning. - Joanna Sliwa 002; Frank Schneider 001; Nathanael Bosch; Agustinus Kristiadi; Philipp Hennig (CoRR, 2024)

Spatiotemporally Coherent Probabilistic Generation of Weather from Climate. - Jonathan Schmidt 001; Luca Schmidt; Felix Strnad; Nicole Ludwig 002; Philipp Hennig (CoRR, 2024)

Parallel-in-Time Probabilistic Numerical ODE Solvers. - Nathanael Bosch; Adrien Corenflos; Fatemeh Yaghoobi; Filip Tronarp; Philipp Hennig; Simo Särkkä (J. Mach. Learn. Res., 2024)

Stable Implementation of Probabilistic ODE Solvers. - Nicholas Krämer; Philipp Hennig (J. Mach. Learn. Res., 2024)

VIPurPCA: Visualizing and Propagating Uncertainty in Principal Component Analysis. - Susanne Zabel; Philipp Hennig; Kay Nieselt (IEEE Trans. Vis. Comput. Graph., 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)

Accelerating neural network training: An analysis of the AlgoPerf competition. - Priya Kasimbeg; Frank Schneider 001; Runa Eschenhagen; Juhan Bae; Chandramouli Shama Sastry; Mark Saroufim; Boyuan Feng; Less Wright; Edward Z. Yang; Zachary Nado; Sourabh Medapati; Philipp Hennig; et al. (ICLR, 2025)

Debiasing Mini-Batch Quadratics for Applications in Deep Learning. - Lukas Tatzel; Bálint Mucsányi; Osane Hackel; Philipp Hennig (ICLR, 2025)

Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods. - Andres Fernandez; Frank Schneider 001; Maren Mahsereci; Philipp Hennig (Trans. Mach. Learn. Res., 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)

Learning convolution operators on compact Abelian groups. - Emilia Magnani; Ernesto De Vito; Philipp Hennig; Lorenzo Rosasco (CoRR, 2025)

Rethinking Approximate Gaussian Inference in Classification. - Bálint Mucsányi; Nathaël Da Costa; Philipp Hennig (CoRR, 2025)

Gaussian Processes and Reproducing Kernels: Connections and Equivalences. - Motonobu Kanagawa; Philipp Hennig; Dino Sejdinovic; Bharath K. Sriperumbudur (CoRR, 2025)

Geometric Gaussian Approximations of Probability Distributions. - Nathaël Da Costa; Bálint Mucsányi; Philipp Hennig (CoRR, 2025)

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