Philipp Hennig

According to our database, Philipp Hennig authored at least 135 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

VIPurPCA: Visualizing and Propagating Uncertainty in Principal Component Analysis.

Susanne Zabel, Philipp Hennig, Kay Nieselt

IEEE Trans. Vis. Comput. Graph., April, 2024

Stable Implementation of Probabilistic ODE Solvers.

Nicholas Krämer, Philipp Hennig

J. Mach. Learn. Res., 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

Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning.

Joanna Sliwa, Frank Schneider, Nathanael Bosch, Agustinus Kristiadi, Philipp Hennig

CoRR, 2024

FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning.

Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler

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

Linearization Turns Neural Operators into Function-Valued Gaussian Processes.

Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig

CoRR, 2024

Scaling up Probabilistic PDE Simulators with Structured Volumetric Information.

Tim Weiland, Marvin Pförtner, Philipp Hennig

CoRR, 2024

Reparameterization invariance in approximate Bayesian inference.

Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg

CoRR, 2024

Flexible inference in heterogeneous and attributed multilayer networks.

Martina Contisciani, Marius Hobbhahn, Eleanor A. Power, Philipp Hennig, Caterina De Bacco

CoRR, 2024

Computation-Aware Kalman Filtering and Smoothing.

Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig

CoRR, 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, 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

CoRR, 2024

Probabilistic ODE solvers for integration error-aware numerical optimal control.

Amon Lahr, Filip Tronarp, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig, Melanie N. Zeilinger

Proceedings of the 6th Annual Learning for Dynamics & Control Conference, 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, 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

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

A Greedy Approximation for k-Determinantal Point Processes.

Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Optimistic Optimization of Gaussian Process Samples.

Julia Grosse, Cheng Zhang, Philipp Hennig

Trans. Mach. Learn. Res., 2023

ViViT: Curvature Access Through The Generalized Gauss-Newton's Low-Rank Structure.

Felix Dangel, Lukas Tatzel, Philipp Hennig

Trans. Mach. Learn. Res., 2023

Sample Path Regularity of Gaussian Processes from the Covariance Kernel.

Nathaël Da Costa, Marvin Pförtner, Lancelot Da Costa, Philipp Hennig

CoRR, 2023

Accelerating Generalized Linear Models by Trading off Computation for Uncertainty.

Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig

CoRR, 2023

Benchmarking Neural Network Training Algorithms.

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

CoRR, 2023

Uncertainty and Structure in Neural Ordinary Differential Equations.

Katharina Ott, Michael Tiemann, Philipp Hennig

CoRR, 2023

Bayesian Numerical Integration with Neural Networks.

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

CoRR, 2023

Baysian numerical integration with neural networks.

Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol

Proceedings of the Uncertainty in Artificial Intelligence, 2023

The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions.

Jonathan Schmidt, Philipp Hennig, Jörg Nick, Filip Tronarp

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Geometry of Neural Nets' Parameter Spaces Under Reparametrization.

Agustinus Kristiadi, Felix Dangel, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures.

Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Probabilistic Exponential Integrators.

Nathanael Bosch, Philipp Hennig, Filip Tronarp

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

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., August, 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

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers.

Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger

CoRR, 2022

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs.

Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig

CoRR, 2022

Fast predictive uncertainty for classification with Bayesian deep networks.

Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Wasserstein t-SNE.

Fynn Bachmann, Philipp Hennig, Dmitry Kobak

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Posterior and Computational Uncertainty in Gaussian Processes.

Jonathan Wenger, Geoff Pleiss, Marvin Pförtner, Philipp Hennig, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks.

Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Preconditioning for Scalable Gaussian Process Hyperparameter Optimization.

Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner

Proceedings of the International Conference on Machine Learning, 2022

Fenrir: Physics-Enhanced Regression for Initial Value Problems.

Filip Tronarp, Nathanael Bosch, Philipp Hennig

Proceedings of the International Conference on Machine Learning, 2022

Probabilistic ODE Solutions in Millions of Dimensions.

Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig

Proceedings of the International Conference on Machine Learning, 2022

Uncertainty in equation learning.

Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius

Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference.

Luca Rendsburg, Agustinus Kristiadi, Philipp Hennig, Ulrike von Luxburg

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Being a Bit Frequentist Improves Bayesian Neural Networks.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations.

Nicholas Krämer, Jonathan Schmidt, Philipp Hennig

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Pick-and-Mix Information Operators for Probabilistic ODE Solvers.

Nathanael Bosch, Filip Tronarp, Philipp Hennig

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Bayesian ODE solvers: the maximum a posteriori estimate.

Filip Tronarp, Simo Särkkä, Philipp Hennig

Stat. Comput., 2021

Robot Learning With Crash Constraints.

Alonso Marco, Dominik Baumann, Majid Khadiv, Philipp Hennig, Ludovic Righetti, Sebastian Trimpe

IEEE Robotics Autom. Lett., 2021

Probabilistic Numerical Methods - From Theory to Implementation (Dagstuhl Seminar 21432).

Philipp Hennig, Ilse C. F. Ipsen, Maren Mahsereci, Tim Sullivan

Dagstuhl Reports, 2021

ProbNum: Probabilistic Numerics in Python.

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

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

Reducing the Variance of Gaussian Process Hyperparameter Optimization with Preconditioning.

Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John P. Cunningham, Jacob R. Gardner

CoRR, 2021

Linear-Time Probabilistic Solutions of Boundary Value Problems.

Nicholas Krämer, Philipp Hennig

CoRR, 2021

Informed Equation Learning.

Matthias Werner, Andrej Junginger, Philipp Hennig, Georg Martius

CoRR, 2021

Laplace Matching for fast Approximate Inference in Generalized Linear Models.

Marius Hobbhahn, Philipp Hennig

CoRR, 2021

A Probabilistically Motivated Learning Rate Adaptation for Stochastic Optimization.

Filip de Roos, Carl Jidling, Adrian Wills, Thomas B. Schön, Philipp Hennig

CoRR, 2021

Learnable uncertainty under Laplace approximations.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Probabilistic DAG search.

Julia Grosse, Cheng Zhang, Philipp Hennig

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks.

Frank Schneider, Felix Dangel, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Probabilistic State Space Model for Joint Inference from Differential Equations and Data.

Jonathan Schmidt, Nicholas Krämer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Linear-Time Probabilistic Solution of Boundary Value Problems.

Nicholas Krämer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Laplace Redux - Effortless Bayesian Deep Learning.

Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers.

Robin M. Schmidt, Frank Schneider, Philipp Hennig

Proceedings of the 38th International Conference on Machine Learning, 2021

High-Dimensional Gaussian Process Inference with Derivatives.

Filip de Roos, Alexandra Gessner, Philipp Hennig

Proceedings of the 38th International Conference on Machine Learning, 2021

Bayesian Quadrature on Riemannian Data Manifolds.

Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis

Proceedings of the 38th International Conference on Machine Learning, 2021

ResNet After All: Neural ODEs and Their Numerical Solution.

Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann

Proceedings of the 9th International Conference on Learning Representations, 2021

Calibrated Adaptive Probabilistic ODE Solvers.

Nathanael Bosch, Philipp Hennig, Filip Tronarp

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Convergence rates of Gaussian ODE filters.

Hans Kersting, Timothy John Sullivan, Philipp Hennig

Stat. Comput., 2020

Conjugate Gradients for Kernel Machines.

Simon Bartels, Philipp Hennig

J. Mach. Learn. Res., 2020

Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

CoRR, 2020

When are Neural ODE Solutions Proper ODEs?

Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann

CoRR, 2020

Probabilistic Linear Solvers for Machine Learning.

Jonathan Wenger, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks.

Agustinus Kristiadi, Matthias Hein, Philipp Hennig

Proceedings of the 37th International Conference on Machine Learning, 2020

Differentiable Likelihoods for Fast Inversion of 'Likelihood-Free' Dynamical Systems.

Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig

Proceedings of the 37th International Conference on Machine Learning, 2020

BackPACK: Packing more into Backprop.

Felix Dangel, Frederik Kunstner, Philipp Hennig

Proceedings of the 8th International Conference on Learning Representations, 2020

Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering.

Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

Integrals over Gaussians under Linear Domain Constraints.

Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Modular Block-diagonal Curvature Approximations for Feedforward Architectures.

Felix Dangel, Stefan Harmeling, Philipp Hennig

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

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

A probabilistic model for the numerical solution of initial value problems.

Michael Schober, Simo Särkkä, Philipp Hennig

Stat. Comput., 2019

Probabilistic linear solvers: a unifying view.

Simon Bartels, Jon Cockayne, Ilse C. F. Ipsen, Philipp Hennig

Stat. Comput., 2019

Classified Regression for Bayesian Optimization: Robot Learning with Unknown Penalties.

Alonso Marco, Dominik Baumann, Philipp Hennig, Sebastian Trimpe

CoRR, 2019

Uncertainty Estimates for Ordinal Embeddings.

Michael Lohaus, Philipp Hennig, Ulrike von Luxburg

CoRR, 2019

Limitations of the Empirical Fisher Approximation.

Frederik Kunstner, Lukas Balles, Philipp Hennig

CoRR, 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 for natural gradient descent.

Frederik Kunstner, Philipp Hennig, Lukas Balles

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Convergence Guarantees for Adaptive Bayesian Quadrature Methods.

Motonobu Kanagawa, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

DeepOBS: A Deep Learning Optimizer Benchmark Suite.

Frank Schneider, Lukas Balles, Philipp Hennig

Proceedings of the 7th International Conference on Learning Representations, 2019

Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization.

Filip de Roos, Philipp Hennig

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Fast and Robust Shortest Paths on Manifolds Learned from Data.

Georgios Arvanitidis, Søren Hauberg, Philipp Hennig, Michael Schober

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences.

Motonobu Kanagawa, Philipp Hennig, Dino Sejdinovic, Bharath K. Sriperumbudur

CoRR, 2018

Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients.

Lukas Balles, Philipp Hennig

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Probabilistic Line Searches for Stochastic Optimization.

Maren Mahsereci, Philipp Hennig

J. Mach. Learn. Res., 2017

Bayesian Filtering for ODEs with Bounded Derivatives.

Emilia Magnani, Hans Kersting, Michael Schober, 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

Krylov Subspace Recycling for Fast Iterative Least-Squares in Machine Learning.

Filip de Roos, Philipp Hennig

CoRR, 2017

Early Stopping without a Validation Set.

Maren Mahsereci, Lukas Balles, Christoph Lassner, Philipp Hennig

CoRR, 2017

Follow the Signs for Robust Stochastic Optimization.

Lukas Balles, Philipp Hennig

CoRR, 2017

Coupling Adaptive Batch Sizes with Learning Rates.

Lukas Balles, Javier Romero, Philipp Hennig

Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, 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, Stefan Schaal, Sebastian Trimpe

Proceedings of the 2017 IEEE International Conference on Robotics and Automation, 2017

On the design of LQR kernels for efficient controller learning.

Alonso Marco, Philipp Hennig, Stefan Schaal, Sebastian Trimpe

Proceedings of the 56th IEEE Annual Conference on Decision and Control, 2017

Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets.

Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

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

Dual Control for Approximate Bayesian Reinforcement Learning.

Edgar D. Klenske, Philipp Hennig

J. Mach. Learn. Res., 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

Exact Sampling from Determinantal Point Processes.

Philipp Hennig, Roman Garnett

CoRR, 2016

Active Uncertainty Calibration in Bayesian ODE Solvers.

Hans Kersting, Philipp Hennig

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Automatic LQR tuning based on Gaussian process global optimization.

Alonso Marco, Philipp Hennig, Jeannette Bohg, Stefan Schaal, Sebastian Trimpe

Proceedings of the 2016 IEEE International Conference on Robotics and Automation, 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

Proceedings of the 15th European Control Conference, 2016

Batch Bayesian Optimization via Local Penalization.

Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Probabilistic Approximate Least-Squares.

Simon Bartels, Philipp Hennig

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Probabilistic Interpretation of Linear Solvers.

Philipp Hennig

SIAM J. Optim., 2015

Probabilistic Numerics and Uncertainty in Computations.

Philipp Hennig, Michael A. Osborne, Mark A. Girolami

CoRR, 2015

A Random Riemannian Metric for Probabilistic Shortest-Path Tractography.

Søren Hauberg, Michael Schober, Matthew G. Liptrot, Philipp Hennig, Aasa Feragen

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 2015

Inference of Cause and Effect with Unsupervised Inverse Regression.

Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

Local Gaussian Regression.

Franziska Meier, Philipp Hennig, Stefan Schaal

CoRR, 2014

Active Learning of Linear Embeddings for Gaussian Processes.

Roman Garnett, Michael A. Osborne, Philipp Hennig

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Probabilistic ODE Solvers with Runge-Kutta Means.

Michael Schober, David Duvenaud, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Incremental Local Gaussian Regression.

Franziska Meier, Philipp Hennig, Stefan Schaal

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature.

Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Probabilistic Shortest Path Tractography in DTI Using Gaussian Process ODE Solvers.

Michael Schober, Niklas Kasenburg, Aasa Feragen, Philipp Hennig, Søren Hauberg

Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014

Efficient Bayesian local model learning for control.

Franziska Meier, Philipp Hennig, Stefan Schaal

Proceedings of the 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014

Probabilistic Progress Bars.

Martin Kiefel, Christian J. Schuler, Philipp Hennig

Proceedings of the Pattern Recognition - 36th German Conference, 2014

Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics.

Philipp Hennig, Søren Hauberg

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Quasi-Newton methods: a new direction.

Philipp Hennig, Martin Kiefel

J. Mach. Learn. Res., 2013

Probabilistic Numerical Analysis in Riemannian Statistics.

Philipp Hennig, Søren Hauberg

CoRR, 2013

The Randomized Dependence Coefficient.

David López-Paz, Philipp Hennig, Bernhard Schölkopf

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Fast Probabilistic Optimization from Noisy Gradients.

Philipp Hennig

Proceedings of the 30th International Conference on Machine Learning, 2013

Nonparametric dynamics estimation for time periodic systems.

Edgar D. Klenske, Melanie Nicole Zeilinger, Bernhard Schölkopf, Philipp Hennig

Proceedings of the 51st Annual Allerton Conference on Communication, 2013

2012

Kernel Topic Models.

Philipp Hennig, David H. Stern, Ralf Herbrich, Thore Graepel

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Entropy Search for Information-Efficient Global Optimization.

Philipp Hennig, Christian J. Schuler

J. Mach. Learn. Res., 2012

Learning tracking control with forward models.

Botond Bocsi, Philipp Hennig, Lehel Csató, Jan Peters

Proceedings of the IEEE International Conference on Robotics and Automation, 2012

2011

Approximate inference in graphical models.

Philipp Hennig

PhD thesis, 2011

Optimal Reinforcement Learning for Gaussian Systems.

Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Coherent Inference on Optimal Play in Game Trees.

Philipp Hennig, David H. Stern, Thore Graepel

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy.

Mark Bangert, Philipp Hennig, Uwe Oelfke

Proceedings of the Ninth International Conference on Machine Learning and Applications, 2010