| Auto-sklearn: efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, JT Springenberg, M Blum, F Hutter Automated Machine Learning, 113-134, 2019 | 1141 | 2019 |
| Auto-sklearn: efficient and robust automated machine learning M Feurer, A Klein, K Eggensperger, JT Springenberg, M Blum, F Hutter Automated Machine Learning, 113-134, 2019 | 1141 | 2019 |
| BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter Proceedings of the 35th International Conference on Machine Learning, 2018 | 312 | 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 | 301 | 2016 |
| Bayesian optimization with robust Bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in neural information processing systems 29, 4134-4142, 2016 | 218 | 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 | 180 | 2019 |
| Towards automatically-tuned neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, F Hutter Workshop on Automatic Machine Learning, 58-65, 2016 | 155 | 2016 |
| Learning curve prediction with Bayesian neural networks A Klein, S Falkner, JT Springenberg, F Hutter International Conference on Learning Representations (ICLR) 2017, 2016 | 106 | 2016 |
| 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 | 77 | 2018 |
| 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 | 67 | 2018 |
| Robo: A flexible and robust bayesian optimization framework in python A Klein, S Falkner, N Mansur, F Hutter NIPS 2017 Bayesian Optimization Workshop, 2017 | 52 | 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 | 52 | 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 | 33 | 2019 |
| Tabular benchmarks for joint architecture and hyperparameter optimization A Klein, F Hutter arXiv preprint arXiv:1905.04970, 2019 | 29 | 2019 |
| Towards efficient Bayesian optimization for big data A Klein, S Bartels, S Falkner, P Hennig, F Hutter NIPS 2015 Bayesian Optimization Workshop, 2015 | 25 | 2015 |
| 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 | 24 | 2017 |
| Combining hyperband and bayesian optimization S Falkner, A Klein, F Hutter NIPS 2017 Bayesian Optimization Workshop (Dec 2017), 2017 | 19 | 2017 |
| Meta-surrogate benchmarking for hyperparameter optimization A Klein, Z Dai, F Hutter, N Lawrence, J Gonzalez arXiv preprint arXiv:1905.12982, 2019 | 18 | 2019 |
| 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 | 18 | 2015 |
| Towards reproducible neural architecture and hyperparameter search A Klein, E Christiansen, K Murphy, F Hutter | 10 | 2018 |
Frank HutterProfessor of Computer Science, University of Freiburg, GermanyVerified email at cs.uni-freiburg.de
