Felix Mohr
Felix Mohr
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Cited by
Cited by
ML-Plan: Automated machine learning via hierarchical planning
F Mohr, M Wever, E Hüllermeier
Machine Learning 107, 1495-1515, 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
Learning Curves for Decision Making in Supervised Machine Learning - A Survey
F Mohr, JN van Rijn
arXiv preprint arXiv:2201.12150, 2022
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
Ml-plan for unlimited-length machine learning pipelines
MD Wever, F Mohr, E Hüllermeier
ICML 2018 AutoML Workshop, 2018
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
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
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
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
Automated multi-label classification based on ML-Plan
M Wever, F Mohr, E Hüllermeier
arXiv preprint arXiv:1811.04060, 2018
Automated online service composition
F Mohr, A Jungmann, HK Büning
2015 IEEE International Conference on Services Computing, 57-64, 2015
Automating multi-label classification extending ml-plan
MD Wever, F Mohr, A Tornede, E Hüllermeier
Towards model selection using learning curve cross-validation
F Mohr, JN van Rijn
8th ICML Workshop on automated machine learning (AutoML), 2021
An approach towards adaptive service composition in markets of composed services
A Jungmann, F Mohr
Journal of Internet Services and Applications 6 (1), 1-18, 2015
Fast and informative model selection using learning curve cross-validation
F Mohr, JN van Rijn
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
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 Competition and Demonstration Track, 2021
Automated machine learning service composition
F Mohr, M Wever, E Hüllermeier
arXiv preprint arXiv:1809.00486, 2018
Ensembles of evolved nested dichotomies for classification
M Wever, F Mohr, E Hüllermeier
Proceedings of the Genetic and Evolutionary Computation Conference, 561-568, 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
Market-specific service compositions: Specification and matching
S Arifulina, F Mohr, G Engels, MC Platenius, W Schäfer
2015 IEEE World Congress on Services, 333-340, 2015
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