Philipp Hennig

ORCID

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 150

Top Venues

University of Cambridge, UK (PHD)

Education History

University of Cambridge, UK

PHD

Publications

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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR (2023)
Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. CoRR (2023)
Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions. NeurIPS (2023)
Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
Bayesian Numerical Integration with Neural Networks. CoRR (2023)
Katharina Ott, Michael Tiemann, Philipp Hennig
Uncertainty and Structure in Neural Ordinary Differential Equations. CoRR (2023)
Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
Baysian numerical integration with neural networks. UAI (2023)
Agustinus Kristiadi, Felix Dangel, Philipp Hennig
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. NeurIPS (2023)
Nathanael Bosch, Philipp Hennig, Filip Tronarp
Probabilistic Exponential Integrators. NeurIPS (2023)
Agustinus Kristiadi, Felix Dangel, Philipp Hennig
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization. CoRR (2023)
Felix Dangel, Lukas Tatzel, Philipp Hennig
ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure. Trans. Mach. Learn. Res. 2023 (2023)
George E. Dahl, Frank Schneider, Zachary Nado, Naman Agarwal, Chandramouli Shama Sastry, Philipp Hennig, Sourabh Medapati, Runa Eschenhagen, Priya Kasimbeg, Daniel Suo, Juhan Bae, Justin Gilmer, Abel L. Peirson, Bilal Khan, Rohan Anil, Mike Rabbat, Shankar Krishnan, Daniel Snider, Ehsan Amid, Kongtao Chen, Chris J. Maddison, Rakshith Vasudev, Michal Badura, Ankush Garg, Peter Mattson
Benchmarking Neural Network Training Algorithms. CoRR (2023)
Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig
Accelerating Generalized Linear Models by Trading off Computation for Uncertainty. CoRR (2023)
Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä
Parallel-in-Time Probabilistic Numerical ODE Solvers. CoRR (2023)
Nathanael Bosch, Philipp Hennig, Filip Tronarp
Probabilistic Exponential Integrators. CoRR (2023)
Nathaël Da Costa, Marvin Pförtner, Lancelot Da Costa, Philipp Hennig
Sample Path Regularity of Gaussian Processes from the Covariance Kernel. CoRR (2023)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS (2023)
Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. AISTATS (2022)
Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. CoRR (2022)
Nathanael Bosch, Filip Tronarp, Philipp Hennig
Pick-and-Mix Information Operators for Probabilistic ODE Solvers. AISTATS (2022)
Fynn Bachmann, Philipp Hennig, Dmitry Kobak
Wasserstein t-SNE. CoRR (2022)
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Posterior and Computational Uncertainty in Gaussian Processes. CoRR (2022)
Filip Tronarp, Nathanael Bosch, Philipp Hennig
Fenrir: Physics-Enhanced Regression for Initial Value Problems. CoRR (2022)
Jonathan Oesterle, Nicholas Krämer, Philipp Hennig, Philipp Berens
Correction to: Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 51 (3) (2022)
Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius
Uncertainty in equation learning. GECCO Companion (2022)
Julia Grosse, Cheng Zhang, Philipp Hennig
Optimistic Optimization of Gaussian Process Samples. CoRR (2022)
Filip Tronarp, Nathanael Bosch, Philipp Hennig
Fenrir: Physics-Enhanced Regression for Initial Value Problems. ICML (2022)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS (2022)
Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham
Posterior and Computational Uncertainty in Gaussian Processes. NeurIPS (2022)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization. ICML (2022)
Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR (2022)
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. NeurIPS (2022)
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. AISTATS (2022)
Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference. CoRR (2022)
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. CoRR (2022)
Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
Fast predictive uncertainty for classification with Bayesian deep networks. UAI (2022)
Fynn Bachmann, Philipp Hennig, Dmitry Kobak
Wasserstein t-SNE. ECML/PKDD (1) (2022)
Jonathan Oesterle, Nicholas Krämer, Philipp Hennig, Philipp Berens
Probabilistic solvers enable a straight-forward exploration of numerical uncertainty in neuroscience models. J. Comput. Neurosci. 50 (4) (2022)
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig
Probabilistic ODE Solutions in Millions of Dimensions. ICML (2022)
Nicholas Krämer, Philipp Hennig
Linear-Time Probabilistic Solution of Boundary Value Problems. NeurIPS (2021)
Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner
Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning. CoRR (2021)
Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann
ResNet After All: Neural ODEs and Their Numerical Solution. ICLR (2021)
Filip de Roos, Alexandra Gessner, Philipp Hennig
High-Dimensional Gaussian Process Inference with Derivatives. CoRR (2021)
Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius
Informed Equation Learning. CoRR (2021)
Filip de Roos, Carl Jidling, Adrian Wills, Thomas B. Schön, Philipp Hennig
A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization. CoRR (2021)
Marius Hobbhahn, Philipp Hennig
Laplace Matching for fast Approximate Inference in Generalized Linear Models. CoRR (2021)
Frank Schneider, Felix Dangel, Philipp Hennig
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. NeurIPS (2021)
Nathanael Bosch, Philipp Hennig, Filip Tronarp
Calibrated Adaptive Probabilistic ODE Solvers. AISTATS (2021)
Nathanael Bosch, Filip Tronarp, Philipp Hennig
Pick-and-Mix Information Operators for Probabilistic ODE Solvers. CoRR (2021)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS (2021)
Julia Grosse, Cheng Zhang, Philipp Hennig
Probabilistic DAG search. UAI (2021)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. CoRR (2021)
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Bayesian Quadrature on Riemannian Data Manifolds. CoRR (2021)
Robin M. Schmidt, Frank Schneider, Philipp Hennig
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. ICML (2021)
Frank Schneider, Felix Dangel, Philipp Hennig
Cockpit: A Practical Debugging Tool for Training Deep Neural Networks. CoRR (2021)
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Learnable uncertainty under Laplace approximations. UAI (2021)
Julia Grosse, Cheng Zhang, Philipp Hennig
Probabilistic DAG Search. CoRR (2021)
Jonathan Wenger, Nicholas Krämer, Marvin Pförtner, Jonathan Schmidt, Nathanael Bosch, Nina Effenberger, Johannes Zenn, Alexandra Gessner, Toni Karvonen, François-Xavier Briol, Maren Mahsereci, Philipp Hennig
ProbNum: Probabilistic Numerics in Python. CoRR (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS (2021)
Nicholas Krämer, Philipp Hennig
Linear-Time Probabilistic Solutions of Boundary Value Problems. CoRR (2021)
Filip Tronarp, Simo Särkkä, Philipp Hennig
Bayesian ODE solvers: the maximum a posteriori estimate. Stat. Comput. 31 (3) (2021)
Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
Bayesian Quadrature on Riemannian Data Manifolds. ICML (2021)
Philipp Hennig, Ilse C. F. Ipsen, Maren Mahsereci, Tim Sullivan
Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432). Dagstuhl Reports 11 (9) (2021)
Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. NeurIPS (2021)
Nicholas Krämer, Jonathan Schmidt, Philipp Hennig
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations. CoRR (2021)
Filip de Roos, Alexandra Gessner, Philipp Hennig
High-Dimensional Gaussian Process Inference with Derivatives. ICML (2021)
Felix Dangel, Lukas Tatzel, Philipp Hennig
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure. CoRR (2021)
Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe
Robot Learning With Crash Constraints. IEEE Robotics Autom. Lett. 6 (2) (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR (2021)
Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data. CoRR (2021)
Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig
Probabilistic ODE Solutions in Millions of Dimensions. CoRR (2021)
Jonathan Wenger, Philipp Hennig
Probabilistic Linear Solvers for Machine Learning. CoRR (2020)
Nathanael Bosch, Philipp Hennig, Filip Tronarp
Calibrated Adaptive Probabilistic ODE Solvers. CoRR (2020)
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. CoRR (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. ICBINB@NeurIPS (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML (2020)
Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe
Robot Learning with Crash Constraints. CoRR (2020)
Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems. ICML (2020)
Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering. CoRR (2020)
Simon Bartels, Philipp Hennig
Conjugate Gradients for Kernel Machines. J. Mach. Learn. Res. 21 (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR (2020)
Hans Kersting, Timothy John Sullivan, Philipp Hennig
Convergence rates of Gaussian ODE filters. Stat. Comput. 30 (6) (2020)
Nicholas Krämer, Philipp Hennig
Stable Implementation of Probabilistic ODE Solvers. CoRR (2020)
Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig
Integrals over Gaussians under Linear Domain Constraints. AISTATS (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Learnable Uncertainty under Laplace Approximations. CoRR (2020)
Robin M. Schmidt, Frank Schneider, Philipp Hennig
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. CoRR (2020)
Felix Dangel, Stefan Harmeling, Philipp Hennig
Modular Block-diagonal Curvature Approximations for Feedforward Architectures. AISTATS (2020)
Filip Tronarp, Simo Särkkä, Philipp Hennig
Bayesian ODE Solvers: The Maximum A Posteriori Estimate. CoRR (2020)
Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann
When are Neural ODE Solutions Proper ODEs? CoRR (2020)
Felix Dangel, Frederik Kunstner, Philipp Hennig
BackPACK: Packing more into Backprop. ICLR (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR (2020)
Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
Fast Predictive Uncertainty for Classification with Bayesian Deep Networks. CoRR (2020)
Jonathan Wenger, Philipp Hennig
Probabilistic Linear Solvers for Machine Learning. NeurIPS (2020)
Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober
Fast and Robust Shortest Paths on Manifolds Learned from Data. CoRR (2019)
Frederik Kunstner, Philipp Hennig, Lukas Balles
Limitations of the empirical Fisher approximation for natural gradient descent. NeurIPS (2019)
Filip de Roos, Philipp Hennig
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization. AISTATS (2019)
Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober
Fast and Robust Shortest Paths on Manifolds Learned from Data. AISTATS (2019)
Motonobu Kanagawa, Philipp Hennig
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. CoRR (2019)
Motonobu Kanagawa, Philipp Hennig
Convergence Guarantees for Adaptive Bayesian Quadrature Methods. NeurIPS (2019)

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Publication Activity (10 Years)

Top Topics

Top Venues

Philipp Hennig