DeepOBS: A Deep Learning Optimizer Benchmark Suite. - Frank Schneider 001; Lukas Balles; Philipp Hennig (ICLR (Poster), 2019)

Descending through a Crowded Valley - Benchmarking Deep Learning Optimizers. - Robin M. Schmidt; Frank Schneider 001; Philipp Hennig (ICML, 2021)

Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks. - Frank Schneider 001; Felix Dangel; Philipp Hennig (NeurIPS, 2021)

Understanding Deep Learning Optimization via Benchmarking and Debugging. - Frank Schneider 001 (2022)

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

Accelerating Generalized Linear Models by Trading off Computation for Uncertainty. - Lukas Tatzel; Jonathan Wenger; Frank Schneider 001; Philipp Hennig (CoRR, 2023)

Efficient Weight-Space Laplace-Gaussian Filtering and Smoothing for Sequential Deep Learning. - Joanna Sliwa 002; Frank Schneider 001; Nathanael Bosch; Agustinus Kristiadi; Philipp Hennig (CoRR, 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)

Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods. - Andres Fernandez; Frank Schneider 001; Maren Mahsereci; Philipp Hennig (Trans. Mach. Learn. Res., 2025)

Accelerating Non-Conjugate Gaussian Processes By Trading Off Computation For Uncertainty. - Lukas Tatzel; Jonathan Wenger; Frank Schneider 001; Philipp Hennig (Trans. Mach. Learn. Res., 2025)