| 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 | 2953 | 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 | 1341 | 2018 |
| 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 | 826 | 2019 |
| 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 | 746 | 2016 |
| Bayesian optimization with robust Bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in neural information processing systems 29, 2016 | 554 | 2016 |
| Towards automatically-tuned neural networks H Mendoza, A Klein, M Feurer, JT Springenberg, F Hutter Workshop on automatic machine learning, 58-65, 2016 | 369 | 2016 |
| Learning curve prediction with Bayesian neural networks A Klein, S Falkner, JT Springenberg, F Hutter International Conference on Learning Representations (ICLR) 2017, 2016 | 278 | 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 | 243 | 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 | 239 | 2018 |
| Tabular benchmarks for joint architecture and hyperparameter optimization A Klein, F Hutter arXiv preprint arXiv:1905.04970, 2019 | 113 | 2019 |
| 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 | 108 | 2017 |
| 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 | 101 | 2021 |
| The Sacred Infrastructure for Computational Research. K Greff, A Klein, M Chovanec, F Hutter, J Schmidhuber SciPy 17, 49-56, 2017 | 99 | 2017 |
| Fast bayesian hyperparameter optimization on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter | 80 | 2017 |
| Model-based asynchronous hyperparameter and neural architecture search A Klein, LC Tiao, T Lienart, C Archambeau, M Seeger arXiv preprint arXiv:2003.10865, 2020 | 53 | 2020 |
| Meta-surrogate benchmarking for hyperparameter optimization A Klein, Z Dai, F Hutter, N Lawrence, J Gonzalez Advances in Neural Information Processing Systems 32, 2019 | 47 | 2019 |
| Automatic Termination for Hyperparameter Optimization A Makarova, H Shen, V Perrone, A Klein, JB Faddoul, A Krause, ... First Conference on Automated Machine Learning (Main Track), 2021 | 46* | 2021 |
| Syne tune: A library for large scale hyperparameter tuning and reproducible research D Salinas, M Seeger, A Klein, V Perrone, M Wistuba, C Archambeau International Conference on Automated Machine Learning, 16/1-23, 2022 | 42 | 2022 |
| Combining hyperband and bayesian optimization S Falkner, A Klein, F Hutter NIPS 2017 Bayesian Optimization Workshop (Dec 2017), 2017 | 39 | 2017 |
| BORE: Bayesian optimization by density-ratio estimation LC Tiao, A Klein, MW Seeger, EV Bonilla, C Archambeau, F Ramos International Conference on Machine Learning, 10289-10300, 2021 | 37* | 2021 |
Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, Germany確認したメール アドレス: cs.uni-freiburg.de
