| ML-Plan: Automated machine learning via hierarchical planning F Mohr, M Wever, E Hüllermeier Machine Learning, 2018 | 281 | 2018 |
| AutoML for Multi-Label Classification: Overview and Empirical Evaluation M Wever, A Tornede, F Mohr, E Hüllermeier IEEE Transactions on pattern analysis and machine intelligence, 3037 - 3054, 2021 | 118 | 2021 |
| A survey of methods for automated algorithm configuration E Schede, J Brandt, A Tornede, M Wever, V Bengs, E Hüllermeier, ... Journal of Artificial Intelligence Research 75, 425-487, 2022 | 102 | 2022 |
| Towards green automated machine learning: Status quo and future directions T Tornede, A Tornede, J Hanselle, F Mohr, M Wever, E Hüllermeier Journal of Artificial Intelligence Research 77, 427-457, 2023 | 86 | 2023 |
| Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning F Mohr, M Wever, A Tornede, E Hüllermeier IEEE Transactions on pattern analysis and machine intelligence, 2021 | 58 | 2021 |
| 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 | 42 | 2024 |
| Algorithm selection on a meta level A Tornede, L Gehring, T Tornede, M Wever, E Hüllermeier Machine Learning 112 (4), 1253-1286, 2023 | 40 | 2023 |
| Naive Automated Machine Learning--A Late Baseline for AutoML F Mohr, M Wever arXiv preprint arXiv:2103.10496, 2021 | 35* | 2021 |
| Extreme Algorithm Selection with Dyadic Feature Representation A Tornede, M Wever, E Hüllermeier Discovery Science 2020, 2020 | 35 | 2020 |
| ML-Plan for unlimited-length machine learning pipelines M Wever, F Mohr, E Hüllermeier ICML 2018 AutoML Workshop, 2018 | 31 | 2018 |
| Hybrid Ranking and Regression for Algorithm Selection J Hanselle, A Tornede, M Wever, E Hüllermeier KI 2020: Advances in Artificial Intelligence: 43rd German Conference on AI …, 2020 | 28 | 2020 |
| AutoML for Predictive Maintenance: One Tool to RUL them all T Tornede, A Tornede, M Wever, F Mohr, E Hüllermeier IoTStream @ ECMLPKDD 2020, 2020 | 26 | 2020 |
| Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis A Tornede, M Wever, S Werner, F Mohr, E Hüllermeier Asian Conference on Machine Learning, 2020, 2020 | 24 | 2020 |
| Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance T Tornede, A Tornede, M Wever, E Hüllermeier Proceedings of the Genetic and Evolutionary Computation Conference, 2021 | 18 | 2021 |
| Automated Multi-Label Classification based on ML-Plan M Wever, F Mohr, E Hüllermeier arXiv preprint arXiv:1811.04060, 2018 | 16 | 2018 |
| Automating Multi-Label Classification Extending ML-Plan M Wever, F Mohr, A Tornede, E Hüllermeier 6th ICML Workshop on Automated Machine Learning, 2019 | 14 | 2019 |
| Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking A Tornede, M Wever, F Mohr, E Hüllermeier | 13 | 2019 |
| Ensembles of Evolved Nested Dichotomies for Classification M Wever, F Mohr, E Hüllermeier Proceedings of the Genetic and Evolutionary Computation Conference, 561-568, 2018 | 12 | 2018 |
| Hyperparameter optimization in deep multi-target prediction D Iliadis, M Wever, B De Baets, W Waegeman arXiv preprint arXiv:2211.04362, 2022 | 11* | 2022 |
| Automated machine learning service composition F Mohr, M Wever, E Hüllermeier arXiv preprint arXiv:1809.00486, 2018 | 11 | 2018 |
Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerified email at upb.de
