Runa Eschenhagen
PhD student, University of Cambridge
re393 [AT] cam.ac.uk
About
I am a PhD student in the Machine Learning Group within the Computational and Biological Learning Lab at the University of Cambridge, supervised by Richard Turner. I am broadly interested in better understanding and improving deep learning. Specifically, I currently work on training algorithms for deep learning.
Previously, I obtained an MSc in Machine Learning from the University of Tübingen and worked as a research assistant in the Methods of Machine Learning group led by Philipp Hennig. I received a BSc in Cognitive Science from the University of Osnabrück and spent my final year as an intern in the Approximate Bayesian Inference Team led by Emtiyaz Khan at RIKEN AIP in Tokyo.
Publications
* / † / ‡ indicate equal contribution. Also, see my Google Scholar page.Kronecker-factored Approximate Curvature for Linear Weight-Sharing Layers
Runa Eschenhagen
MSc Thesis, University of Tübingen, 2023
Natural Gradient Variational Inference for Continual Learning in Deep Neural Networks
Runa Eschenhagen
BSc Thesis, University of Osnabrück, 2019
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
NeurIPS 2023 (spotlight)
Benchmarking Neural Network Training Algorithms
George E. Dahl*, Frank Schneider*, Zachary Nado*, Naman Agarwal*, Chandramouli Shama Sastry†, Philipp Hennig†, Sourabh Medapati†, Runa Eschenhagen†, Priya Kasimbeg†, Daniel Suo†, Juhan Bae†, Justin Gilmer†, Abel L. Peirson†, Bilal Khan†, Rohan Anil†, Mike Rabbat†, Shankar Krishnan†, Daniel Snider‡, Ehsan Amid‡, Kongtao Chen‡, Chris J. Maddison‡, Rakshith Vasudev‡, Michal Badura‡, Ankush Garg‡, Peter Mattson‡
Preprint, 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
AABI, 2023
Kronecker-factored Approximate Curvature for Linear Weight-Sharing Layers
Runa Eschenhagen
MSc Thesis, University of Tübingen, 2023
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
NeurIPS 2022
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs
Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig
Preprint, 2022
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi
Bayesian Deep Learning Workshop, NeurIPS 2021
Laplace Redux—Effortless Bayesian Deep Learning
Erik Daxberger*, Agustinus Kristiadi*, Alexander Immer*, Runa Eschenhagen*, Matthias Bauer, Philipp Hennig
NeurIPS 2021
Continual Deep Learning by Functional Regularisation of Memorable Past
Pingbo Pan*, Siddharth Swaroop*, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
NeurIPS 2020
Natural Gradient Variational Inference for Continual Learning in Deep Neural Networks
Runa Eschenhagen
BSc Thesis, University of Osnabrück, 2019
Practical Deep Learning with Bayesian Principles
Kazuki Osawa, Siddharth Swaroop*, Anirudh Jain*, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
NeurIPS 2019