Practical Deep Learning with Bayesian Principles. - Kazuki Osawa; Siddharth Swaroop; Mohammad Emtiyaz Khan; Anirudh Jain; Runa Eschenhagen; Richard E. Turner; Rio Yokota (NeurIPS, 2019)

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)

Laplace Redux - Effortless Bayesian Deep Learning. - Erik A. Daxberger; Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Matthias Bauer 001; Philipp Hennig (NeurIPS, 2021)

Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning. - Runa Eschenhagen; Erik A. Daxberger; Philipp Hennig; Agustinus Kristiadi (CoRR, 2021)

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 (CoRR, 2022)

Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures. - Runa Eschenhagen; Alexander Immer; Richard E. Turner; Frank Schneider 001; Philipp Hennig (NeurIPS, 2023)

Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization. - Agustinus Kristiadi; Alexander Immer; Runa Eschenhagen; Vincent Fortuin (CoRR, 2023)

Benchmarking Neural Network Training Algorithms. - George E. Dahl; Frank Schneider 001; Zachary Nado; Naman Agarwal; Chandramouli Shama Sastry; Philipp Hennig; Sourabh Medapati; Runa Eschenhagen; Priya Kasimbeg; Daniel Suo; Juhan Bae; Justin Gilmer; et al. (CoRR, 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)

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 (ICML, 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 (ICML, 2024)

Accelerating neural network training: An analysis of the AlgoPerf competition. - Priya Kasimbeg; Frank Schneider 001; Runa Eschenhagen; Juhan Bae; Chandramouli Shama Sastry; Mark Saroufim; Boyuan Feng; Less Wright; Edward Z. Yang; Zachary Nado; Sourabh Medapati; Philipp Hennig; et al. (ICLR, 2025)

Influence Functions for Scalable Data Attribution in Diffusion Models. - Bruno Kacper Mlodozeniec; Runa Eschenhagen; Juhan Bae; Alexander Immer; David Krueger 001; Richard E. Turner (ICLR, 2025)

Position: Curvature Matrices Should Be Democratized via Linear Operators. - Felix Dangel; Runa Eschenhagen; Weronika Ormaniec; Andres Fernandez; Lukas Tatzel; Agustinus Kristiadi (CoRR, 2025)

Spectral-factorized Positive-definite Curvature Learning for NN Training. - Wu Lin; Felix Dangel; Runa Eschenhagen; Juhan Bae; Richard E. Turner; Roger B. Grosse (CoRR, 2025)

Purifying Shampoo: Investigating Shampoo's Heuristics by Decomposing its Preconditioner. - Runa Eschenhagen; Aaron Defazio; Tsung-Hsien Lee; Richard E. Turner; Hao-Jun Michael Shi (CoRR, 2025)

Kronecker-factored Approximate Curvature (KFAC) From Scratch. - Felix Dangel; Bálint Mucsányi; Tobias Weber; Runa Eschenhagen (CoRR, 2025)