Empirical Inference Conference Paper Modular Block-diagonal Curvature Approximations for Feedforward Architectures Dangel, F., Harmeling, S., Hennig, P. Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108:799-808, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (Published) URL BibTeX

Empirical Inference Conference Paper Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Kristiadi, A., Hein, M., Hennig, P. Proceedings of the 37th International Conference on Machine Learning (ICML), 119:5436-5446, Proceedings of Machine Learning Research, (Editors: Hal Daumé III and Aarti Singh), PMLR, July 2020 (Published) URL BibTeX

Empirical Inference Conference Paper Differentiable Likelihoods for Fast Inversion of ‘Likelihood-Free’ Dynamical Systems Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Tiemann, M., Hennig, P. Proceedings of the 37th International Conference on Machine Learning (ICML), 119:5154-5164, Proceedings of Machine Learning Research, (Editors: Hal Daumé III and Aarti Singh), Curran Associates, Inc., Red Hook, NY, Titel 37th International Conference on Machine Learning (ICML 2020), July 2020 (Published) URL BibTeX

Empirical Inference Conference Paper BackPACK: Packing more into Backprop Dangel, F., Kunstner, F., Hennig, P. 8th International Conference on Learning Representations (ICLR), April 2020 (Published) URL BibTeX

Empirical Inference Probabilistic Numerics Article Analytical probabilistic modeling of dose-volume histograms Wahl, N., Hennig, P., Wieser, H., Bangert, M. Medical Physics, 47(10):5260-5273, 2020 (Published) DOI BibTeX

Empirical Inference Article Conjugate Gradients for Kernel Machines Bartels, S., Hennig, P. Journal of Machine Learning Research, 21(55):1-42, 2020 (Published) URL BibTeX

Empirical Inference Probabilistic Numerics Article Convergence rates of Gaussian ODE filters Kersting, H., Sullivan, T. J., Hennig, P. Statistics and Computing, 30(6):1791-1816, 2020 (Published) DOI BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Convergence Guarantees for Adaptive Bayesian Quadrature Methods Kanagawa, M., Hennig, P. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 6234-6245, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX

Empirical Inference Probabilistic Numerics Conference Paper Limitations of the empirical Fisher approximation for natural gradient descent Kunstner, F., Hennig, P., Balles, L. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 4158-4169, (Editors: H. Wallach and H. Larochelle and A. Beygelzimer and F. d’Alché-Buc and E. Fox and R. Garnett), Curran Associates, Inc., 33rd Annual Conference on Neural Information Processing Systems, December 2019 (Published) URL BibTeX

Probabilistic Numerics Article Probabilistic Solutions To Ordinary Differential Equations As Non-Linear Bayesian Filtering: A New Perspective Tronarp, F., Kersting, H., Särkkä, S., Hennig, P. Statistics and Computing, 29(6):1297-1315, 2019 (Published) DOI URL BibTeX