ML Anthology

Hennig, Philipp

86 publications

ICLR 2025 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
TMLR 2025 Accelerating Non-Conjugate Gaussian Processes by Trading Off Computation for Uncertainty Lukas Tatzel, Jonathan Wenger, Frank Schneider, Philipp Hennig
TMLR 2025 Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig
AISTATS 2025 Computation-Aware Kalman Filtering and Smoothing Marvin Pförtner, Jonathan Wenger, Jon Cockayne, Philipp Hennig
TMLR 2025 Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale Using Sketched Methods Andres Fernandez, Frank Schneider, Maren Mahsereci, Philipp Hennig
ICLR 2025 Debiasing Mini-Batch Quadratics for Applications in Deep Learning Lukas Tatzel, Bálint Mucsányi, Osane Hackel, Philipp Hennig
AISTATS 2025 Flexible and Efficient Probabilistic PDE Solvers Through Gaussian Markov Random Fields Tim Weiland, Marvin Pförtner, Philipp Hennig
ICML 2025 Linearization Turns Neural Operators into Function-Valued Gaussian Processes Emilia Magnani, Marvin Pförtner, Tobias Weber, Philipp Hennig
NeurIPS 2025 Rethinking Approximate Gaussian Inference in Classification Bálint Mucsányi, Nathaël Da Costa, Philipp Hennig
AISTATS 2024 A Greedy Approximation for K-Determinantal Point Processes Julia Grosse, Rahel Fischer, Roman Garnett, Philipp Hennig
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
ICML 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
NeurIPS 2024 FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning Tristan Cinquin, Marvin Pförtner, Vincent Fortuin, Philipp Hennig, Robert Bamler
JMLR 2024 Parallel-in-Time Probabilistic Numerical ODE Solvers Nathanael Bosch, Adrien Corenflos, Fatemeh Yaghoobi, Filip Tronarp, Philipp Hennig, Simo Särkkä
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 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
L4DC 2024 Probabilistic ODE Solvers for Integration Error-Aware Numerical Optimal Control Amon Lahr, Filip Tronarp, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig, Melanie N. Zeilinger
NeurIPS 2024 Reparameterization Invariance in Approximate Bayesian Inference Hrittik Roy, Marco Miani, Carl Henrik Ek, Philipp Hennig, Marvin Pförtner, Lukas Tatzel, Søren Hauberg
JMLR 2024 Stable Implementation of Probabilistic ODE Solvers Nicholas Krämer, Philipp Hennig
ICLRW 2024 Uncertainty Quantification for Fourier Neural Operators Tobias Weber, Emilia Magnani, Marvin Pförtner, Philipp Hennig
UAI 2023 Bayesian Numerical Integration with Neural Networks Katharina Ott, Michael Tiemann, Philipp Hennig, François-Xavier Briol
NeurIPS 2023 Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures Runa Eschenhagen, Alexander Immer, Richard Turner, Frank Schneider, Philipp Hennig
TMLR 2023 Optimistic Optimization of Gaussian Process Samples Julia Grosse, Cheng Zhang, Philipp Hennig
NeurIPS 2023 Probabilistic Exponential Integrators Nathanael Bosch, Philipp Hennig, Filip Tronarp
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, Philipp Hennig, Jörg Nick, Filip Tronarp
TMLR 2023 ViViT: Curvature Access Through the Generalized Gauss-Newton’s Low-Rank Structure Felix Dangel, Lukas Tatzel, Philipp Hennig
AISTATS 2022 Being a Bit Frequentist Improves Bayesian Neural Networks Agustinus Kristiadi, Matthias Hein, 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 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, Philipp Hennig
UAI 2022 Fast Predictive Uncertainty for Classification with Bayesian Deep Networks Marius Hobbhahn, Agustinus Kristiadi, Philipp Hennig
ICML 2022 Fenrir: Physics-Enhanced Regression for Initial Value Problems Filip Tronarp, Nathanael Bosch, Philipp Hennig
NeurIPSW 2022 Late-Phase Second-Order Training Lukas Tatzel, Philipp Hennig, Frank Schneider
NeurIPS 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
ICML 2022 Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner
ICML 2022 Probabilistic ODE Solutions in Millions of Dimensions Nicholas Krämer, Nathanael Bosch, Jonathan Schmidt, Philipp Hennig
ECML-PKDD 2022 Wasserstein T-SNE Fynn Bachmann, Philipp Hennig, Dmitry Kobak
AISTATS 2021 Calibrated Adaptive Probabilistic ODE Solvers Nathanael Bosch, Philipp Hennig, Filip Tronarp
NeurIPS 2021 A Probabilistic State Space Model for Joint Inference from Differential Equations and Data Jonathan Schmidt, Nicholas Krämer, Philipp Hennig
NeurIPS 2021 An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence Agustinus Kristiadi, Matthias Hein, Philipp Hennig
ICML 2021 Bayesian Quadrature on Riemannian Data Manifolds Christian Fröhlich, Alexandra Gessner, Philipp Hennig, Bernhard Schölkopf, Georgios Arvanitidis
NeurIPS 2021 Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks Frank Schneider, Felix Dangel, Philipp Hennig
ICML 2021 Descending Through a Crowded Valley - Benchmarking Deep Learning Optimizers Robin M Schmidt, Frank Schneider, Philipp Hennig
ICML 2021 High-Dimensional Gaussian Process Inference with Derivatives Filip Roos, Alexandra Gessner, Philipp Hennig
NeurIPS 2021 Laplace Redux - Effortless Bayesian Deep Learning Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
UAI 2021 Learnable Uncertainty Under Laplace Approximations Agustinus Kristiadi, Matthias Hein, Philipp Hennig
NeurIPS 2021 Linear-Time Probabilistic Solution of Boundary Value Problems Nicholas Krämer, Philipp Hennig
UAI 2021 Probabilistic DAG Search Julia Grosse, Cheng Zhang, Philipp Hennig
ICLR 2021 ResNet After All: Neural ODEs and Their Numerical Solution Katharina Ott, Prateek Katiyar, Philipp Hennig, Michael Tiemann
AISTATS 2020 Modular Block-Diagonal Curvature Approximations for Feedforward Architectures Felix Dangel, Stefan Harmeling, Philipp Hennig
ICLR 2020 BackPACK: Packing More into Backprop Felix Dangel, Frederik Kunstner, Philipp Hennig
ICML 2020 Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi, Matthias Hein, Philipp Hennig
JMLR 2020 Conjugate Gradients for Kernel Machines Simon Bartels, Philipp Hennig
ICML 2020 Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems Hans Kersting, Nicholas Krämer, Martin Schiegg, Christian Daniel, Michael Tiemann, Philipp Hennig
AISTATS 2020 Integrals over Gaussians Under Linear Domain Constraints Alexandra Gessner, Oindrila Kanjilal, Philipp Hennig
NeurIPS 2020 Probabilistic Linear Solvers for Machine Learning Jonathan Wenger, Philipp Hennig
NeurIPSW 2020 Self-Tuning Stochastic Optimization with Curvature-Aware Gradient Filtering Ricky T. Q. Chen, Dami Choi, Lukas Balles, David Duvenaud, Philipp Hennig
AISTATS 2019 Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization Filip Roos, Philipp Hennig
NeurIPS 2019 Convergence Guarantees for Adaptive Bayesian Quadrature Methods Motonobu Kanagawa, Philipp Hennig
ICLR 2019 DeepOBS: A Deep Learning Optimizer Benchmark Suite Frank Schneider, Lukas Balles, Philipp Hennig
AISTATS 2019 Fast and Robust Shortest Paths on Manifolds Learned from Data Georgios Arvanitidis, Soren Hauberg, Philipp Hennig, Michael Schober
NeurIPS 2019 Limitations of the Empirical Fisher Approximation for Natural Gradient Descent Frederik Kunstner, Philipp Hennig, Lukas Balles
ICML 2018 Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients Lukas Balles, Philipp Hennig
UAI 2017 Coupling Adaptive Batch Sizes with Learning Rates Lukas Balles, Javier Romero, Philipp Hennig
AISTATS 2017 Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets Aaron Klein, Stefan Falkner, Simon Bartels, Philipp Hennig, Frank Hutter
JMLR 2017 Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
UAI 2016 Active Uncertainty Calibration in Bayesian ODE Solvers Hans Kersting, Philipp Hennig
AISTATS 2016 Batch Bayesian Optimization via Local Penalization Javier González, Zhenwen Dai, Philipp Hennig, Neil D. Lawrence
JMLR 2016 Dual Control for Approximate Bayesian Reinforcement Learning Edgar D. Klenske, Philipp Hennig
AISTATS 2016 Probabilistic Approximate Least-Squares Simon Bartels, Philipp Hennig
AISTATS 2015 Inference of Cause and Effect with Unsupervised Inverse Regression Eleni Sgouritsa, Dominik Janzing, Philipp Hennig, Bernhard Schölkopf
NeurIPS 2015 Probabilistic Line Searches for Stochastic Optimization Maren Mahsereci, Philipp Hennig
UAI 2014 Active Learning of Linear Embeddings for Gaussian Processes Roman Garnett, Michael A. Osborne, Philipp Hennig
NeurIPS 2014 Incremental Local Gaussian Regression Franziska Meier, Philipp Hennig, Stefan Schaal
NeurIPS 2014 Probabilistic ODE Solvers with Runge-Kutta Means Michael Schober, David K. Duvenaud, Philipp Hennig
AISTATS 2014 Probabilistic Solutions to Differential Equations and Their Application to Riemannian Statistics Philipp Hennig, Søren Hauberg
NeurIPS 2014 Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
ICML 2013 Fast Probabilistic Optimization from Noisy Gradients Philipp Hennig
JMLR 2013 Quasi-Newton Method: A New Direction Philipp Hennig, Martin Kiefel
NeurIPS 2013 The Randomized Dependence Coefficient David Lopez-Paz, Philipp Hennig, Bernhard Schölkopf
JMLR 2012 Entropy Search for Information-Efficient Global Optimization Philipp Hennig, Christian J. Schuler
AISTATS 2012 Kernel Topic Models Philipp Hennig, David Stern, Ralf Herbrich, Thore Graepel
ICML 2012 Quasi-Newton Methods: A New Direction Philipp Hennig, Martin Kiefel
NeurIPS 2011 Optimal Reinforcement Learning for Gaussian Systems Philipp Hennig
AISTATS 2010 Coherent Inference on Optimal Play in Game Trees Philipp Hennig, David Stern, Thore Graepel