BOHB: Robust and efficient hyperparameter optimization at scale S Falkner, A Klein, F Hutter International Conference on Machine Learning, 1437-1446, 2018 | 1204 | 2018 |
Fast bayesian optimization of machine learning hyperparameters on large datasets A Klein, S Falkner, S Bartels, P Hennig, F Hutter Artificial Intelligence and Statistics, 528-536, 2017 | 703 | 2017 |
Bayesian optimization with robust Bayesian neural networks JT Springenberg, A Klein, S Falkner, F Hutter Advances in neural information processing systems 29, 2016 | 518 | 2016 |
Learning curve prediction with Bayesian neural networks A Klein, S Falkner, JT Springenberg, F Hutter International conference on learning representations, 2022 | 256 | 2022 |
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter | 252* | |
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 | 223 | 2018 |
Auto-sklearn 2.0: The next generation M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter arXiv preprint arXiv:2007.04074 24, 8, 2020 | 149 | 2020 |
Practical automated machine learning for the automl challenge 2018 M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter International Workshop on Automatic Machine Learning at ICML, 1189-1232, 2018 | 108 | 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 | 103 | 2017 |
Learning to Design RNA F Runge, D Stoll, S Falkner, F Hutter arXiv preprint arXiv:1812.11951, 2018 | 80 | 2018 |
Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization M Volpp, LP Fröhlich, K Fischer, A Doerr, S Falkner, F Hutter, C Daniel arXiv preprint arXiv:1904.02642, 2019 | 77 | 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 | 76 | 2017 |
Smac v3: Algorithm configuration in python M Lindauer, K Eggensperger, M Feurer, S Falkner, A Biedenkapp, ... URL https://github. com/automl/SMAC3, 2017 | 64 | 2017 |
Weak limit of the three-state quantum walk on the line S Falkner, S Boettcher Physical Review A 90 (1), 012307, 2014 | 52 | 2014 |
Combining hyperband and bayesian optimization S Falkner, A Klein, F Hutter Proceedings of the 31st Conference on Neural Information Processing Systems …, 2017 | 39 | 2017 |
Optimizing neural networks for patent classification L Abdelgawad, P Kluegl, E Genc, S Falkner, F Hutter Joint European Conference on Machine Learning and Knowledge Discovery in …, 2019 | 36 | 2019 |
One-dimensional coinless quantum walks R Portugal, S Boettcher, S Falkner Physical Review A 91 (5), 052319, 2015 | 31 | 2015 |
Towards efficient Bayesian Optimization for Big Data A Klein, S Bartels, S Falkner, P Hennig, F Hutter NIPS 2015 workshop on Bayesian Optimization (BayesOpt 2015), 2015 | 31 | 2015 |
SpySMAC: Automated Configuration and Performance Analysis of SAT Solvers S Falkner, M Lindauer, F Hutter International Conference on Theory and Applications of Satisfiability …, 2015 | 25 | 2015 |
Relation between random walks and quantum walks S Boettcher, S Falkner, R Portugal Physical Review A 91 (5), 052330, 2015 | 25 | 2015 |