| Efficient and Robust Automated Machine Learning M Feurer, A Klein, K Eggensperger, J Springenberg, M Blum, F Hutter Advances in Neural Information Processing Systems, 2962-2970, 2015 | 2985 | 2015 |
| Hyperparameter Optimization M Feurer, F Hutter AutoML: Methods, Sytems, Challenges, 3-37, 2019 | 1855 | 2019 |
| Initializing bayesian hyperparameter optimization via meta-learning M Feurer, J Springenberg, F Hutter Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 676* | 2015 |
| Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning M Feurer, K Eggensperger, S Falkner, M Lindauer, F Hutter Journal of Machine Learning Research 23 (261), 1-61, 2022 | 641* | 2022 |
| Towards an empirical foundation for assessing bayesian optimization of hyperparameters K Eggensperger, M Feurer, F Hutter, J Bergstra, J Snoek, H Hoos, ... NIPS workshop on Bayesian Optimization in Theory and Practice, 1-5, 2013 | 478 | 2013 |
| SMAC3: A versatile Bayesian optimization package for hyperparameter optimization M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, D Deng, ... Journal of Machine Learning Research 23 (54), 1-9, 2022 | 457* | 2022 |
| Towards Automatically-Tuned Deep Neural Networks H Mendoza, A Klein, M Feurer, JT Springenberg, M Urban, M Burkart, ... Automated Machine Learning, 135-149, 2019 | 375* | 2019 |
| OpenML Benchmarking Suites B Bischl, G Casalicchio, M Feurer, P Gijsbers, F Hutter, M Lang, ... Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021 | 238* | 2021 |
| Practical Transfer Learning for Bayesian Optimization M Feurer, B Letham, F Hutter, E Bakshy arXiv:1802.02219v3, 2022 | 175* | 2022 |
| OpenML-Python: an extensible Python API for OpenML M Feurer, JN van Rijn, A Kadra, P Gijsbers, N Mallik, S Ravi, A Müller, ... Journal of Machine Learning Research 22 (100), 1-5, 2021 | 111 | 2021 |
| 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, ... Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021 | 108 | 2021 |
| BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters M Lindauer, K Eggensperger, M Feurer, A Biedenkapp, J Marben, ... arXiv preprint arXiv:1908.06756, 2019 | 54 | 2019 |
| PFNs4BO: In-Context Learning for Bayesian Optimization S Müller, M Feurer, N Hollmann, F Hutter 40th International Conference on Machine Learning, 2023 | 33 | 2023 |
| Can fairness be automated? guidelines and opportunities for fairness-aware automl H Weerts, F Pfisterer, M Feurer, K Eggensperger, E Bergman, N Awad, ... Journal of Artificial Intelligence Research 79, 639-677, 2024 | 23 | 2024 |
| OpenML-CTR23–A curated tabular regression benchmarking suite SF Fischer, M Feurer, B Bischl AutoML Conference 2023 (Workshop), 2023 | 23 | 2023 |
| Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters M Lindauer, M Feurer, K Eggensperger, A Biedenkapp, F Hutter arXiv preprint arXiv:1908.06674, 2019 | 23 | 2019 |
| Mind the Gap: Measuring Generalization Performance Across Multiple Objectives M Feurer, K Eggensperger, E Bergman, F Pfisterer, B Bischl, F Hutter International Symposium on Intelligent Data Analysis, 130-142, 2023 | 7 | 2023 |
| Position: Why We Must Rethink Empirical Research in Machine Learning M Herrmann, FJD Lange, K Eggensperger, G Casalicchio, M Wever, ... Forty-first International Conference on Machine Learning, 2024 | 6* | 2024 |
| Squirrel: A Switching Hyperparameter Optimizer N Awad, G Shala, D Deng, N Mallik, M Feurer, K Eggensperger, ... arXiv preprint arXiv:2012.08180, 2020 | 6 | 2020 |
| Interpretable machine learning for TabPFN D Rundel, J Kobialka, C von Crailsheim, M Feurer, T Nagler, D Rügamer World Conference on Explainable Artificial Intelligence, 465-476, 2024 | 5 | 2024 |
Frank HutterELLIS Institute Tübingen & Professor, University of Freiburg, Germany在 cs.uni-freiburg.de 的电子邮件经过验证
