Runa Eschenhagen

According to our database, Runa Eschenhagen authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Influence Functions for Scalable Data Attribution in Diffusion Models.

Bruno Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, Richard E. Turner

CoRR, 2024

Structured Inverse-Free Natural Gradient Descent: Memory-Efficient & Numerically-Stable KFAC.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective.

Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets.

Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani

CoRR, 2023

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

CoRR, 2023

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization.

Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin

CoRR, 2023

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures.

Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs.

Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig

CoRR, 2022

Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks.

Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning.

Runa Eschenhagen, Erik A. Daxberger, Philipp Hennig, Agustinus Kristiadi

CoRR, 2021

Laplace Redux - Effortless Bayesian Deep Learning.

Erik A. Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Continual Deep Learning by Functional Regularisation of Memorable Past.

Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Practical Deep Learning with Bayesian Principles.

Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019