| Efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in neural information processing systems 28, 2015 | 2227 | 2015 |
| BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter Proceedings of the 35th International Conference on Machine Learning, 2018 | 900 | 2018 |
| Fast Bayesian optimization of machine learning hyperparameters on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter Proceedings of the 20th International Conference on Artificial Intelligence …, 2016 | 582 | 2016 |
| Nas-bench-101: Towards reproducible neural architecture search C Ying, A Klein, E Christiansen, E Real, K Murphy, F Hutter International Conference on Machine Learning, 7105-7114, 2019 | 520 | 2019 |
| Bayesian optimization with robust Bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in neural information processing systems 29, 2016 | 418 | 2016 |
| Towards automatically-tuned neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, F Hutter Workshop on automatic machine learning, 58-65, 2016 | 235 | 2016 |
| Learning curve prediction with Bayesian neural networks A Klein, S Falkner, JT Springenberg, F Hutter International Conference on Learning Representations (ICLR) 2017, 2016 | 209 | 2016 |
| Towards automated deep learning: Efficient joint neural architecture and hyperparameter search A Zela, A Klein, S Falkner, F Hutter arXiv preprint arXiv:1807.06906, 2018 | 184 | 2018 |
| Uncertainty estimates and multi-hypotheses networks for optical flow E Ilg, O Cicek, S Galesso, A Klein, O Makansi, F Hutter, T Brox Proceedings of the European Conference on Computer Vision (ECCV), 652-667, 2018 | 179 | 2018 |
| The sacred infrastructure for computational research K Greff, A Klein, M Chovanec, F Hutter, J Schmidhuber Proceedings of the 16th python in science conference 28, 49-56, 2017 | 84 | 2017 |
| Robo: A flexible and robust bayesian optimization framework in python A Klein, S Falkner, N Mansur, F Hutter NIPS 2017 Bayesian optimization workshop, 4-9, 2017 | 82 | 2017 |
| Tabular benchmarks for joint architecture and hyperparameter optimization A Klein, F Hutter arXiv preprint arXiv:1905.04970, 2019 | 79 | 2019 |
| Towards automatically-tuned deep neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ... Automated machine learning: Methods, systems, challenges, 135-149, 2019 | 76 | 2019 |
| Fast bayesian hyperparameter optimization on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter | 66 | 2017 |
| Meta-surrogate benchmarking for hyperparameter optimization A Klein, Z Dai, F Hutter, N Lawrence, J Gonzalez Advances in Neural Information Processing Systems 32, 2019 | 39 | 2019 |
| HPOBench: A collection of reproducible multi-fidelity benchmark problems for HPO K Eggensperger, P Müller, N Mallik, M Feurer, R Sass, A Klein, N Awad, ... arXiv preprint arXiv:2109.06716, 2021 | 33 | 2021 |
| Towards efficient Bayesian optimization for big data A Klein, S Bartels, S Falkner, P Hennig, F Hutter NIPS 2015 Bayesian Optimization Workshop, 2015 | 30 | 2015 |
| Model-based asynchronous hyperparameter and neural architecture search A Klein, LC Tiao, T Lienart, C Archambeau, M Seeger arXiv preprint arXiv:2003.10865, 2020 | 28* | 2020 |
| Combining hyperband and bayesian optimization S Falkner, A Klein, F Hutter NIPS 2017 Bayesian Optimization Workshop (Dec 2017), 2017 | 28 | 2017 |
| Methods for improving bayesian optimization for automl M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Proceedings of the International Conference on Machine Learning, 2015 | 21 | 2015 |
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
