Runa Eschenhagen

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Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Agustinus KristiadiPostdoc, Vector InstituteVerified email at vectorinstitute.ai
Alexander ImmerPhD student, ETH Zürich, Max Planck Institute for Intelligent SystemsVerified email at inf.ethz.ch
Mohammad Emtiyaz KhanCenter for Advanced Intelligence Project (AIP), RIKEN, TokyoVerified email at postman.riken.jp
Richard E TurnerProfessor, University of CambridgeVerified email at cam.ac.uk
Siddharth SwaroopPostdoctoral Fellow, Harvard UniversityVerified email at seas.harvard.edu
Erik DaxbergerPhD at University of Cambridge & MPI for Intelligent SystemsVerified email at cam.ac.uk
Kazuki OsawaGoogle DeepMindVerified email at google.com
Anirudh JainDoctoral Candidate, Aalto UniversityVerified email at am.ism.ac.in
Rio YokotaAssociate Professor, Tokyo Institute of TechnologyVerified email at gsic.titech.ac.jp
Matthias BauerDeepMind, LondonVerified email at google.com

Runa Eschenhagen

Verified email at cam.ac.uk


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Practical deep learning with Bayesian principles K Osawa, S Swaroop, A Jain, R Eschenhagen, RE Turner, R Yokota, ... NeurIPS 2019, 2019	198	2019
Laplace Redux--Effortless Bayesian Deep Learning E Daxberger, A Kristiadi, A Immer, R Eschenhagen, M Bauer, ... NeurIPS 2021, 2021	112	2021
Continual deep learning by functional regularisation of memorable past P Pan, S Swaroop, A Immer, R Eschenhagen, RE Turner, ME Khan NeurIPS 2020, 2020	80	2020
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning R Eschenhagen, E Daxberger, P Hennig, A Kristiadi Bayesian Deep Learning Workshop, NeurIPS 2021, 2021	12	2021
Natural Gradient Variational Inference for Continual Learning in Deep Neural Networks R Eschenhagen University of Osnabrück, 2019	1	2019
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization A Kristiadi, A Immer, R Eschenhagen, V Fortuin arXiv preprint arXiv:2304.08309, 2023		2023
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs E Magnani, N Krämer, R Eschenhagen, L Rosasco, P Hennig arXiv preprint arXiv:2208.01565, 2022		2022
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks A Kristiadi, R Eschenhagen, P Hennig NeurIPS 2022, 2022		2022

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