Philipp Hennig

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