| 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 | 1832 | 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 | 667 | 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 | 485 | 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 | 373 | 2019 |
| Bayesian optimization with robust Bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in neural information processing systems 29, 2016 | 352 | 2016 |
| Towards automatically-tuned neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, F Hutter Workshop on automatic machine learning, 58-65, 2016 | 202 | 2016 |
| Learning curve prediction with Bayesian neural networks A Klein, S Falkner, JT Springenberg, F Hutter International Conference on Learning Representations (ICLR) 2017, 2016 | 168 | 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 | 143 | 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 | 128 | 2018 |
| 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 | 76 | 2017 |
| 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 | 66 | 2017 |
| Towards automatically-tuned deep neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ... Automated machine learning, 135-149, 2019 | 61 | 2019 |
| Tabular benchmarks for joint architecture and hyperparameter optimization A Klein, F Hutter arXiv preprint arXiv:1905.04970, 2019 | 58 | 2019 |
| Fast bayesian hyperparameter optimization on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter Electronic Journal of Statistics 11 (2), 4945-4968, 2017 | 49 | 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 | 34 | 2019 |
| Towards efficient Bayesian optimization for big data A Klein, S Bartels, S Falkner, P Hennig, F Hutter NIPS 2015 Bayesian Optimization Workshop, 2015 | 28 | 2015 |
| Combining hyperband and bayesian optimization S Falkner, A Klein, F Hutter NIPS 2017 Bayesian Optimization Workshop (Dec 2017), 2017 | 23 | 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 | 20 | 2015 |
| Practical hyperparameter optimization for deep learning S Falkner, A Klein, F Hutter | 18 | 2018 |
| Model-based asynchronous hyperparameter and neural architecture search A Klein, LC Tiao, T Lienart, C Archambeau, M Seeger arXiv preprint arXiv:2003.10865, 2020 | 15* | 2020 |
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
