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Philipp Hennig

72 papers · 2010–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (19) 🧭 Keyword Pioneer πŸƒ Academic Marathon (15) 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) 🐝 Cross-Pollinator (13) 🏠 Conference Loyalist (23) πŸ”¬ Deep Specialist (32) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Keyword Champion (5) πŸ‘₯ Mega-Team (25) 🀝 Dynamic Duo (10) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (16) ⚑ Prolific Year (9) πŸ’Ž Century Club (72) πŸ—ƒοΈ Keyword Collector (78)

Conferences

NIPS (23) AISTATS (19) ICML (13) JMLR (7) ICLR (5) UAI (4) L4DC (1)

Top co-authors

Agustinus Kristiadi (10) Nathanael Bosch (8) Filip Tronarp (7) Marvin PfΓΆrtner (6) Nicholas KrΓ€mer (6) Frank Schneider (5) Jonathan Schmidt (5) Jonathan Wenger (5) Felix Dangel (4) Runa Eschenhagen (4)

Research topics

Probability (2) Statistics (1)

Keywords

gaussian process (19) uncertainty quantification (16) bayesian inference (12) ordinary differential equation (8) bayesian optimization (8) probabilistic numerical method (8) laplace approximation (7) probabilistic numerics (7) variational inference (7) bayesian neural network (6) stochastic gradient descent (5) bayesian quadrature (5) hyperparameter optimization (5) approximate inference (4) numerical integration (4) probabilistic inference (4) neural network optimization (4) gaussian process regression (3) dynamical system (3) posterior approximation (3)

Papers

Computation-Aware Kalman Filtering and Smoothing AISTATS 2025
Linearization Turns Neural Operators into Function-Valued Gaussian Processes ICML 2025
Debiasing Mini-Batch Quadratics for Applications in Deep Learning ICLR 2025
Accelerating neural network training: An analysis of the AlgoPerf competition ICLR 2025
Flexible and Efficient Probabilistic PDE Solvers through Gaussian Markov Random Fields AISTATS 2025
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations ICML 2024
Reparameterization invariance in approximate Bayesian inference NIPS 2024
FSP-Laplace: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning NIPS 2024
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference NIPS 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI ICML 2024
Probabilistic ODE solvers for integration error-aware numerical optimal control L4DC 2024
A Greedy Approximation for k-Determinantal Point Processes AISTATS 2024
Parallel-in-Time Probabilistic Numerical ODE Solvers JMLR 2024
Stable Implementation of Probabilistic ODE Solvers JMLR 2024
Probabilistic Exponential Integrators NIPS 2023
Bayesian numerical integration with neural networks UAI 2023
The Geometry of Neural Nets' Parameter Spaces Under Reparametrization NIPS 2023
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures NIPS 2023
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions NIPS 2023
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations AISTATS 2022
Being a Bit Frequentist Improves Bayesian Neural Networks AISTATS 2022
Fast predictive uncertainty for classification with Bayesian deep networks UAI 2022
Posterior and Computational Uncertainty in Gaussian Processes NIPS 2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks NIPS 2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization ICML 2022
Fenrir: Physics-Enhanced Regression for Initial Value Problems ICML 2022
Pick-and-Mix Information Operators for Probabilistic ODE Solvers AISTATS 2022
Discovering Inductive Bias with Gibbs Priors: A Diagnostic Tool for Approximate Bayesian Inference AISTATS 2022
Probabilistic ODE Solutions in Millions of Dimensions ICML 2022
Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers ICML 2021
Linear-Time Probabilistic Solution of Boundary Value Problems NIPS 2021
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data NIPS 2021
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence NIPS 2021
Laplace Redux - Effortless Bayesian Deep Learning NIPS 2021
Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks NIPS 2021
Calibrated Adaptive Probabilistic ODE Solvers AISTATS 2021
ResNet After All: Neural ODEs and Their Numerical Solution ICLR 2021
High-Dimensional Gaussian Process Inference with Derivatives ICML 2021
Bayesian Quadrature on Riemannian Data Manifolds ICML 2021
Learnable uncertainty under Laplace approximations UAI 2021
Probabilistic DAG search UAI 2021
Integrals over Gaussians under Linear Domain Constraints AISTATS 2020
Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems ICML 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks ICML 2020
Modular Block-diagonal Curvature Approximations for Feedforward Architectures AISTATS 2020
Probabilistic Linear Solvers for Machine Learning NIPS 2020
BackPACK: Packing more into Backprop ICLR 2020
Conjugate Gradients for Kernel Machines JMLR 2020
Limitations of the empirical Fisher approximation for natural gradient descent NIPS 2019
DeepOBS: A Deep Learning Optimizer Benchmark Suite ICLR 2019
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization AISTATS 2019
Fast and Robust Shortest Paths on Manifolds Learned from Data AISTATS 2019
Convergence Guarantees for Adaptive Bayesian Quadrature Methods NIPS 2019
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients ICML 2018
Fast Bayesian Optimization of Machine Learning Hyperparameters on Large Datasets AISTATS 2017
Probabilistic Line Searches for Stochastic Optimization JMLR 2017
Probabilistic Approximate Least-Squares AISTATS 2016
Batch Bayesian Optimization via Local Penalization AISTATS 2016
Dual Control for Approximate Bayesian Reinforcement Learning JMLR 2016
Probabilistic Line Searches for Stochastic Optimization NIPS 2015
Inference of Cause and Effect with Unsupervised Inverse Regression AISTATS 2015
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics AISTATS 2014
Incremental Local Gaussian Regression NIPS 2014
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature NIPS 2014
Probabilistic ODE Solvers with Runge-Kutta Means NIPS 2014
Fast Probabilistic Optimization from Noisy Gradients ICML 2013
The Randomized Dependence Coefficient NIPS 2013
Quasi-Newton Method: A New Direction JMLR 2013
Kernel Topic Models AISTATS 2012
Entropy Search for Information-Efficient Global Optimization JMLR 2012
Optimal Reinforcement Learning for Gaussian Systems NIPS 2011
Coherent Inference on Optimal Play in Game Trees AISTATS 2010