| 2025 | AISTATS | Computation-Aware Kalman Filtering and Smoothing. | Marvin Pfrtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig |
| 2025 | AISTATS | Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields. | Tim Weiland, Marvin Pfrtner, Philipp Hennig |
| 2025 | ICLR | Accelerating neural network training: An analysis of the AlgoPerf competition. | Priya Kasimbeg, Frank Schneider, Runa Eschenhagen, Juhan Bae, Chandramouli Shama Sastry, Mark Saroufim, Boyuan Feng, Less Wright, Edward Z. Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, George E. Dahl |
| 2025 | ICLR | Debiasing Mini-Batch Quadratics for Applications in Deep Learning. | Lukas Tatzel, Blint Mucsnyi, Osane Hackel, Philipp Hennig |
| 2025 | ICML | Linearization Turns Neural Operators into Function-Valued Gaussian Processes. | Emilia Magnani, Marvin Pfrtner, Tobias Weber, Philipp Hennig |
| 2024 | AISTATS | A Greedy Approximation for k-Determinantal Point Processes. | Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig |
| 2024 | ICML | 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 |
| 2024 | ICML | 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, Jos Miguel Hernndez-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rgamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang |
| 2023 | UAI | Baysian numerical integration with neural networks. | Katharina Ott, Michael Tiemann, Philipp Hennig, Franois-Xavier Briol |
| 2022 | AISTATS | Pick-and-Mix Information Operators for Probabilistic ODE Solvers. | Nathanael Bosch, Filip Tronarp, Philipp Hennig |
| 2022 | AISTATS | Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. | Nicholas Krmer, Jonathan Schmidt, Philipp Hennig |
| 2022 | AISTATS | Being a Bit Frequentist Improves Bayesian Neural Networks. | Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
| 2022 | AISTATS | Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. | Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg |
| 2022 | GECCO | Uncertainty in equation learning. | Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius |
| 2022 | ICML | Probabilistic ODE Solutions in Millions of Dimensions. | Nicholas Krmer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig |
| 2022 | ICML | Fenrir: Physics-Enhanced Regression for Initial Value Problems. | Filip Tronarp, Nathanael Bosch, Philipp Hennig |
| 2022 | ICML | Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. | Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner |
| 2022 | UAI | Fast predictive uncertainty for classification with Bayesian deep networks. | Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig |
| 2021 | AISTATS | Calibrated Adaptive Probabilistic ODE Solvers. | Nathanael Bosch, Philipp Hennig, Filip Tronarp |
| 2021 | ICLR | ResNet After All: Neural ODEs and Their Numerical Solution. | Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann |
| 2021 | ICML | Bayesian Quadrature on Riemannian Data Manifolds. | Christian Frhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schlkopf, Georgios Arvanitidis |
| 2021 | ICML | High-Dimensional Gaussian Process Inference with Derivatives. | Filip de Roos, Alexandra Gessner, Philipp Hennig |
| 2021 | ICML | Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. | Robin M. Schmidt, Frank Schneider, Philipp Hennig |
| 2021 | UAI | Probabilistic DAG search. | Julia Grosse, Cheng Zhang, Philipp Hennig |
| 2021 | UAI | Learnable uncertainty under Laplace approximations. | Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
| 2020 | AISTATS | Modular Block-diagonal Curvature Approximations for Feedforward Architectures. | Felix Dangel, Stefan Harmeling, Philipp Hennig |
| 2020 | AISTATS | Integrals over Gaussians under Linear Domain Constraints. | Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig |
| 2020 | ICLR | BackPACK: Packing more into Backprop. | Felix Dangel, Frederik Kunstner, Philipp Hennig |
| 2020 | ICML | Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. | Hans Kersting, Nicholas Krmer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig |
| 2020 | ICML | Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. | Agustinus Kristiadi, Matthias Hein, Philipp Hennig |
| 2019 | AISTATS | Fast and Robust Shortest Paths on Manifolds Learned from Data. | Georgios Arvanitidis, Sren Hauberg, Philipp Hennig, Michael Schober |
| 2019 | AISTATS | Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. | Filip de Roos, Philipp Hennig |
| 2019 | ICLR | DeepOBS: A Deep Learning Optimizer Benchmark Suite. | Frank Schneider, Lukas Balles, Philipp Hennig |
| 2018 | ICML | Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients. | Lukas Balles, Philipp Hennig |
| 2017 | AISTATS | Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets. | Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter |
| 2017 | ICRA | 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, Stefan Schaal, Sebastian Trimpe |
| 2017 | UAI | Coupling Adaptive Batch Sizes with Learning Rates. | Lukas Balles, Javier Romero, Philipp Hennig |
| 2016 | AISTATS | Probabilistic Approximate Least-Squares. | Simon Bartels, Philipp Hennig |
| 2016 | AISTATS | Batch Bayesian Optimization via Local Penalization. | Javier Gonzlez, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence |
| 2016 | ICRA | Automatic LQR tuning based on Gaussian process global optimization. | Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe |
| 2016 | UAI | Active Uncertainty Calibration in Bayesian ODE Solvers. | Hans Kersting, Philipp Hennig |
| 2015 | AISTATS | Inference of Cause and Effect with Unsupervised Inverse Regression. | Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schlkopf |
| 2015 | MICCAI | A Random Riemannian Metric for Probabilistic Shortest-Path Tractography. | Sren Hauberg, Michael Schober, Matthew G. Liptrot, Philipp Hennig, Aasa Feragen |
| 2014 | AISTATS | Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics. | Philipp Hennig, Sren Hauberg |
| 2014 | IROS | Efficient Bayesian local model learning for control. | Franziska Meier, Philipp Hennig, Stefan Schaal |
| 2014 | MICCAI | Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers. | Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, Sren Hauberg |
| 2014 | UAI | Active Learning of Linear Embeddings for Gaussian Processes. | Roman Garnett, Michael A. Osborne, Philipp Hennig |
| 2013 | ICML | Fast Probabilistic Optimization from Noisy Gradients. | Philipp Hennig |
| 2012 | ICML | Quasi-Newton Methods: A New Direction. | Philipp Hennig, Martin Kiefel |
| 2012 | ICRA | Learning tracking control with forward models. | Botond Bocsi, Philipp Hennig, Lehel Csat, Jan Peters |
| 2010 | ICMLA | Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy. | Mark Bangert, Philipp Hennig, Uwe Oelfke |