| ML-Plan: Automated machine learning via hierarchical planning F Mohr, M Wever, E Hüllermeier Machine Learning 107, 1495-1515, 2018 | 246 | 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 43 (9), 3037-3054, 2021 | 81 | 2021 |
| Learning Curves for Decision Making in Supervised Machine Learning - A Survey F Mohr, JN van Rijn arXiv preprint arXiv:2201.12150, 2022 | 68 | 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 | 47 | 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 43 (9), 3055-3066, 2021 | 39 | 2021 |
| Meta-album: Multi-domain meta-dataset for few-shot image classification I Ullah, D Carrión-Ojeda, S Escalera, I Guyon, M Huisman, F Mohr, ... Advances in Neural Information Processing Systems 35, 3232-3247, 2022 | 37 | 2022 |
| Fast and informative model selection using learning curve cross-validation F Mohr, JN van Rijn IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (8), 9669-9680, 2023 | 35 | 2023 |
| Ml-plan for unlimited-length machine learning pipelines MD Wever, F Mohr, E Hüllermeier ICML 2018 AutoML Workshop, 2018 | 30 | 2018 |
| LCDB 1.0: An extensive learning curves database for classification tasks F Mohr, TJ Viering, M Loog, JN van Rijn Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 22 | 2022 |
| Automl for predictive maintenance: One tool to rul them all T Tornede, A Tornede, M Wever, F Mohr, E Hüllermeier IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile …, 2020 | 21 | 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, 737-752, 2020 | 18 | 2020 |
| Towards model selection using learning curve cross-validation F Mohr, JN van Rijn 8th ICML Workshop on automated machine learning (AutoML), 2021 | 17 | 2021 |
| Automated online service composition F Mohr, A Jungmann, HK Büning 2015 IEEE International Conference on Services Computing, 57-64, 2015 | 17 | 2015 |
| Automated multi-label classification based on ML-Plan M Wever, F Mohr, E Hüllermeier arXiv preprint arXiv:1811.04060, 2018 | 16 | 2018 |
| Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification A El Baz, I Ullah, E Alcobaça, AC Carvalho, H Chen, F Ferreira, H Gouk, ... NeurIPS 2021 Competitions and Demonstrations Track, 80-96, 2022 | 14 | 2022 |
| Automating multi-label classification extending ml-plan MD Wever, F Mohr, A Tornede, E Hüllermeier | 14 | 2019 |
| Naive automated machine learning F Mohr, M Wever Machine Learning 112 (4), 1131-1170, 2023 | 12 | 2023 |
| Automated machine learning service composition F Mohr, M Wever, E Hüllermeier arXiv preprint arXiv:1809.00486, 2018 | 12 | 2018 |
| An approach towards adaptive service composition in markets of composed services A Jungmann, F Mohr Journal of Internet Services and Applications 6, 1-18, 2015 | 12 | 2015 |
| The bigger fish: a comparison of meta-learning qsar models on low-resourced aquatic toxicity regression tasks T Schlender, M Viljanen, JN van Rijn, F Mohr, WJGM Peijnenburg, ... Environmental Science & Technology 57 (46), 17818-17830, 2023 | 11 | 2023 |
Marcel WeverLUHAI, Leibniz University HannoverBestätigte E-Mail-Adresse bei ai.uni-hannover.de
