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Felix Mohr
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2020 – today
- 2024
- [j9]Romain Egele
, Felix Mohr
, Tom J. Viering
, Prasanna Balaprakash:
The unreasonable effectiveness of early discarding after one epoch in neural network hyperparameter optimization. Neurocomputing 597: 127964 (2024) - [c27]Lionel Kielhöfer, Felix Mohr
, Jan N. van Rijn
:
Learning Curve Extrapolation Methods Across Extrapolation Settings. IDA (2) 2024: 145-157 - [i15]Romain Egele, Felix Mohr, Tom J. Viering, Prasanna Balaprakash:
The Unreasonable Effectiveness Of Early Discarding After One Epoch In Neural Network Hyperparameter Optimization. CoRR abs/2404.04111 (2024) - 2023
- [j8]Tanja Tornede, Alexander Tornede, Jonas Hanselle, Felix Mohr, Marcel Wever, Eyke Hüllermeier:
Towards Green Automated Machine Learning: Status Quo and Future Directions. J. Artif. Intell. Res. 77: 427-457 (2023) - [j7]Tanja Tornede
, Alexander Tornede
, Lukas Fehring, Lukas Gehring, Helena Graf, Jonas Hanselle
, Felix Mohr, Marcel Wever:
PyExperimenter: Easily distribute experiments and track results. J. Open Source Softw. 8(86): 5149 (2023) - [j6]Felix Mohr
, Marcel Wever:
Naive automated machine learning. Mach. Learn. 112(4): 1131-1170 (2023) - [j5]Felix Mohr
, Jan N. van Rijn
:
Fast and Informative Model Selection Using Learning Curve Cross-Validation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 9669-9680 (2023) - [c26]Haozhe Sun
, Isabelle Guyon, Felix Mohr, Hedi Tabia:
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network. IJCNN 2023: 1-9 - [i14]Tanja Tornede, Alexander Tornede, Lukas Fehring, Lukas Gehring, Helena Graf, Jonas Hanselle, Felix Mohr, Marcel Wever:
PyExperimenter: Easily distribute experiments and track results. CoRR abs/2301.06348 (2023) - [i13]Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu:
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. CoRR abs/2302.08909 (2023) - [i12]Haozhe Sun, Isabelle Guyon, Felix Mohr, Hedi Tabia:
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network. CoRR abs/2310.01157 (2023) - 2022
- [c25]Pavel Brazdil, Jan N. van Rijn, Henry Gouk, Felix Mohr:
Advances in Metalearning: ECML/PKDD Workshop on Meta-Knowledge Transfer. Meta-Knowledge Transfer @ ECML/PKDD 2022: 1-7 - [c24]Ihsan Ullah, Dustin Carrión-Ojeda, Sergio Escalera, Isabelle Guyon, Mike Huisman, Felix Mohr, Jan N. van Rijn, Haozhe Sun, Joaquin Vanschoren, Phan Anh Vu:
Meta-Album: Multi-domain Meta-Dataset for Few-Shot Image Classification. NeurIPS 2022 - [c23]Felix Mohr, Tom J. Viering, Marco Loog, Jan N. van Rijn:
LCDB 1.0: An Extensive Learning Curves Database for Classification Tasks. ECML/PKDD (5) 2022: 3-19 - [e1]Pavel Brazdil, Jan N. van Rijn, Henry Gouk, Felix Mohr:
ECML/PKDD Workshop on Meta-Knowledge Transfer, 23 September 2022, Grenoble, France. Proceedings of Machine Learning Research 191, PMLR 2022 [contents] - [i11]Felix Mohr, Jan N. van Rijn:
Learning Curves for Decision Making in Supervised Machine Learning - A Survey. CoRR abs/2201.12150 (2022) - [i10]Adrian El Baz, André C. P. L. F. de Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Shell Hu, Frank Hutter, Zhengying Liu, Felix Mohr, Jan N. van Rijn, Xin Wang, Isabelle Guyon:
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. CoRR abs/2206.08138 (2022) - 2021
- [j4]Marcel Wever
, Alexander Tornede
, Felix Mohr
, Eyke Hüllermeier
:
AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3037-3054 (2021) - [j3]Felix Mohr
, Marcel Wever
, Alexander Tornede
, Eyke Hüllermeier
:
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3055-3066 (2021) - [c22]Felix Mohr, Viktor Bengs, Eyke Hüllermeier:
Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI 2021: 12373-12381 - [c21]Dustin Carrión-Ojeda, Mahbubul Alam, Sergio Escalera, Ahmed K. Farahat, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz, Chetan Gupta, Isabelle Guyon, Joël Roman Ky, Xian Yeow Lee, Xin Liu, Felix Mohr, Manh Hung Nguyen, Emmanuel Pintelas, Stefan Roth, Simone Schaub-Meyer, Haozhe Sun
, Ihsan Ullah, Joaquin Vanschoren, Lasitha Vidyaratne, Jiamin Wu, Xiaotian Yin:
NeurIPS’22 Cross-Domain MetaDL Challenge: Results and lessons learned. NeurIPS (Competition and Demos) 2021: 50-72 - [c20]Adrian El Baz, Ihsan Ullah, Edesio Alcobaça, André C. P. L. F. de Carvalho, Hong Chen, Fabio Ferreira, Henry Gouk, Chaoyu Guan, Isabelle Guyon, Timothy M. Hospedales, Shell Hu, Mike Huisman, Frank Hutter, Zhengying Liu, Felix Mohr, Ekrem Öztürk, Jan N. van Rijn, Haozhe Sun
, Xin Wang, Wenwu Zhu:
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. NeurIPS (Competition and Demos) 2021: 80-96 - [i9]Felix Mohr, Gonzalo Mejía, Francisco Yuraszeck:
Single and Parallel Machine Scheduling with Variable Release Dates. CoRR abs/2103.01785 (2021) - [i8]Felix Mohr, Marcel Wever:
Naive Automated Machine Learning - A Late Baseline for AutoML. CoRR abs/2103.10496 (2021) - [i7]Eyke Hüllermeier, Felix Mohr, Alexander Tornede, Marcel Wever:
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. CoRR abs/2109.04744 (2021) - [i6]Tanja Tornede, Alexander Tornede, Jonas Hanselle, Marcel Wever, Felix Mohr, Eyke Hüllermeier:
Towards Green Automated Machine Learning: Status Quo and Future Directions. CoRR abs/2111.05850 (2021) - [i5]Felix Mohr, Jan N. van Rijn:
Fast and Informative Model Selection using Learning Curve Cross-Validation. CoRR abs/2111.13914 (2021) - [i4]Felix Mohr, Marcel Wever:
Naive Automated Machine Learning. CoRR abs/2111.14514 (2021) - 2020
- [c19]Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier:
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020: 737-752 - [c18]Marcel Wever
, Alexander Tornede, Felix Mohr, Eyke Hüllermeier:
LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. IDA 2020: 561-573 - [c17]Tanja Tornede
, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier:
AutoML for Predictive Maintenance: One Tool to RUL Them All. IoT Streams/ITEM@PKDD/ECML 2020: 106-118 - [i3]Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier:
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. CoRR abs/2007.02816 (2020)
2010 – 2019
- 2019
- [c16]Felix Mohr, Marcel Wever
, Alexander Tornede, Eyke Hüllermeier:
From Automated to On-The-Fly Machine Learning. GI-Jahrestagung 2019: 273-274 - 2018
- [j2]Felix Mohr, Marcel Wever
, Eyke Hüllermeier:
ML-Plan: Automated machine learning via hierarchical planning. Mach. Learn. 107(8-10): 1495-1515 (2018) - [c15]Felix Mohr, Marcel Wever
, Eyke Hüllermeier:
On-the-Fly Service Construction with Prototypes. SCC 2018: 225-232 - [c14]Felix Mohr, Marcel Wever
, Eyke Hüllermeier, Amin Faez:
(WIP) Towards the Automated Composition of Machine Learning Services. SCC 2018: 241-244 - [c13]Marcel Wever
, Felix Mohr, Eyke Hüllermeier:
Ensembles of evolved nested dichotomies for classification. GECCO 2018: 561-568 - [c12]Felix Mohr, Marcel Wever
, Eyke Hüllermeier:
Reduction Stumps for Multi-class Classification. IDA 2018: 225-237 - [i2]Felix Mohr, Marcel Wever, Eyke Hüllermeier:
Automated Machine Learning Service Composition. CoRR abs/1809.00486 (2018) - [i1]Marcel Wever, Felix Mohr, Eyke Hüllermeier:
Automated Multi-Label Classification based on ML-Plan. CoRR abs/1811.04060 (2018) - 2017
- [b2]Felix Mohr:
Towards automated service composition under quality constraints. Paderborn University, Germany, 2017 - [c11]Felix Mohr, Theo Lettmann, Eyke Hüllermeier:
Planning with Independent Task Networks. KI 2017: 193-206 - 2016
- [b1]Felix Mohr:
Automated Software and Service Composition - A Survey and Evaluating Review. Springer Briefs in Computer Science, Springer 2016, ISBN 978-3-319-34167-5, pp. 1-104 - 2015
- [j1]Alexander Jungmann, Felix Mohr:
An approach towards adaptive service composition in markets of composed services. J. Internet Serv. Appl. 6(1): 5:1-5:18 (2015) - [c10]Felix Mohr, Alexander Jungmann, Hans Kleine Büning:
Automated Online Service Composition. SCC 2015: 57-64 - [c9]Felix Mohr, Sven Walther:
Template-Based Generation of Semantic Services. ICSR 2015: 188-203 - [c8]Felix Mohr:
A Metric for Functional Reusability of Services. ICSR 2015: 298-313 - [c7]Svetlana Arifulina, Felix Mohr, Gregor Engels, Marie Christin Platenius, Wilhelm Schäfer:
Market-Specific Service Compositions: Specification and Matching. SERVICES 2015: 333-340 - 2014
- [c6]Felix Mohr:
Estimating Functional Reusability of Services. ICSOC 2014: 411-418 - [c5]Felix Mohr:
Issues of automated software composition in AI planning. ASE 2014: 895-898 - [c4]Alexander Jungmann, Felix Mohr, Bernd Kleinjohann:
Combining Automatic Service Composition with Adaptive Service Recommendation for Dynamic Markets of Services. SERVICES 2014: 346-353 - [c3]Alexander Jungmann, Felix Mohr, Bernd Kleinjohann:
Applying Reinforcement Learning for Resolving Ambiguity in Service Composition. SOCA 2014: 105-112 - 2013
- [c2]Felix Mohr, Hans Kleine Büning:
Semi-Automated Software Composition Through Generated Components. iiWAS 2013: 676 - [c1]Felix Mohr, Theodor Lettmann, Hans Kleine Büning:
Reducing Nondeterminism in Automated Service Composition. SOCA 2013: 154-161
Coauthor Index
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last updated on 2024-07-05 21:04 CEST by the dblp team
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