Runa Eschenhagen

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 16

Top Topics

Dempster Shafer

Weakly Supervised

Natural Gradient

Top Venues

Publications

Wu Lin, Felix Dangel, Runa Eschenhagen, Juhan Bae, Richard E. Turner, Alireza Makhzani
Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective. CoRR (2024)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. CoRR (2023)
Wu Lin, Felix Dangel, Runa Eschenhagen, Kirill Neklyudov, Agustinus Kristiadi, Richard E. Turner, Alireza Makhzani
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC for Large Neural Nets. CoRR (2023)
Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. CoRR (2023)
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
Benchmarking Neural Network Training Algorithms. CoRR (2023)
Runa Eschenhagen, Alexander Immer, Richard E. Turner, Frank Schneider, Philipp Hennig
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. NeurIPS (2023)
Emilia Magnani, Nicholas Krämer, Runa Eschenhagen, Lorenzo Rosasco, Philipp Hennig
Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs. CoRR (2022)
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. NeurIPS (2022)
Agustinus Kristiadi, Runa Eschenhagen, Philipp Hennig
Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks. CoRR (2022)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. NeurIPS (2021)
Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, Philipp Hennig
Laplace Redux - Effortless Bayesian Deep Learning. CoRR (2021)
Runa Eschenhagen, Erik Daxberger, Philipp Hennig, Agustinus Kristiadi
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. CoRR (2021)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. NeurIPS (2020)
Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard E. Turner, Mohammad Emtiyaz Khan
Continual Deep Learning by Functional Regularisation of Memorable Past. CoRR (2020)
Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
Practical Deep Learning with Bayesian Principles. CoRR (2019)
Kazuki Osawa, Siddharth Swaroop, Mohammad Emtiyaz Khan, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota
Practical Deep Learning with Bayesian Principles. NeurIPS (2019)