| Probabilistic line searches for stochastic optimization M Mahsereci, P Hennig Journal of Machine Learning Research 18 (119), 1-59, 2017 | 167 | 2017 |
| Early stopping without a validation set M Mahsereci, L Balles, C Lassner, P Hennig arXiv preprint arXiv:1703.09580, 2017 | 155 | 2017 |
| Emulation of physical processes with emukit A Paleyes, M Pullin, M Mahsereci, N Lawrence, J González Second Workshop on Machine Learning and the Physical Sciences, NeurIPS, 2019 | 148 | 2019 |
| Active Multi-Information Source Bayesian Quadrature A Gessner, J Gonzalez, M Mahsereci Conference on Uncertainty in Artificial Intelligence (UAI), 2019 | 41 | 2019 |
| Emukit: A Python toolkit for decision making under uncertainty A Paleyes, M Mahsereci, ND Lawrence Proceedings of the Python in Science Conference 8, 2023 | 31 | 2023 |
| ProbNum: Probabilistic Numerics in Python J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ... arXiv preprint arXiv:2112.02100, 2021 | 20 | 2021 |
| Dynamic pruning of a neural network via gradient signal-to-noise ratio JN Siems, A Klein, C Archambeau, M Mahsereci 8th ICML Workshop on Automated Machine Learning (AutoML), 2021 | 8 | 2021 |
| Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood M Naslidnyk, M Kanagawa, T Karvonen, M Mahsereci arXiv preprint arXiv:2307.07466, 2023 | 6 | 2023 |
| Early stopping without a validation set. arXiv 2017 M Mahsereci, L Balles, C Lassner, P Hennig arXiv preprint arXiv:1703.09580, 0 | 6 | |
| A dictionary of closed-form kernel mean embeddings FX Briol, A Gessner, T Karvonen, M Mahsereci arXiv preprint arXiv:2504.18830, 2025 | 5 | 2025 |
| A Fourier State Space Model for Bayesian ODE Filters H Kersting, M Mahsereci arXiv preprint arXiv:2007.09118, 2020 | 5 | 2020 |
| Spectral analysis of PG 1034+ 001, the exciting star of Hewett 1 M Mahsereci, E Ringat, T Rauch, K Werner, JW Kruk Planetary Nebulae: An Eye to the Future 283, 426-427, 2012 | 5 | 2012 |
| Invariant Priors for Bayesian Quadrature M Naslidnyk, J Gonzalez, M Mahsereci arXiv preprint arXiv:2112.01578, 2021 | 4 | 2021 |
| Automating stochastic optimization with gradient variance estimates L Balles, M Mahsereci, P Hennig ICML AutoML Workshop, 13, 2017 | 4 | 2017 |
| Comparing Scale Parameter Estimators for Gaussian Process Interpolation with the Brownian Motion Prior: Leave-One-Out Cross Validation and Maximum Likelihood M Naslidnyk, M Kanagawa, T Karvonen, M Mahsereci SIAM/ASA Journal on Uncertainty Quantification 13 (2), 679-717, 2025 | 3 | 2025 |
| ProbNum: probabilistic numerics in python,(2021) J Wenger, N Krämer, M Pförtner, J Schmidt, N Bosch, N Effenberger, ... arXiv preprint arXiv:2112.02100, 0 | 3 | |
| Probabilistic Approaches to Stochastic Optimization M Mahsereci Eberhard Karls Universität Tübingen Tübingen, 2018 | 2 | 2018 |
| Connecting Parameter Magnitudes and Hessian Eigenspaces at Scale using Sketched Methods A Fernandez, F Schneider, M Mahsereci, P Hennig arXiv preprint arXiv:2504.14701, 2025 | 1 | 2025 |
| What Apples Tell About Oranges: Connecting Pruning Masks and Hessian Eigenspaces P Hennig, M Mahsereci, F Schneider, A Fernandez OpenReview. net, 2023 | | 2023 |
| Probabilistic numerical methods-from theory to implementation (Dagstuhl Seminar 21432) P Hennig, ICF Ipsen, M Mahsereci, TJ Sullivan Dagstuhl Reports 11 (9), 102-119, 2022 | | 2022 |
