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Marcel Wever
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2020 – today
- 2024
- [j12]Dimitrios Iliadis
, Marcel Wever
Hyperparameter optimization of two-branch neural networks in multi-target prediction. Appl. Soft Comput. 165: 111957 (2024) - [j11]Stefan Heid, Marcel Wever, Eyke Hüllermeier:
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. J. Data Min. Digit. Humanit. 2024 (2024) - [c19]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. ICML 2024 - [c18]Jasmin Brandt, Marcel Wever, Viktor Bengs, Eyke Hüllermeier:
Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. IJCAI 2024: 3742-3750 - [c17]Raphael Fischer
MetaQuRe: Meta-learning from Model Quality and Resource Consumption. ECML/PKDD (7) 2024: 209-226 - [i22]Pritha Gupta, Marcel Wever, Eyke Hüllermeier:
Information Leakage Detection through Approximate Bayes-optimal Prediction. CoRR abs/2401.14283 (2024) - [i21]Marcel Wever:
Automated Machine Learning for Multi-Label Classification. CoRR abs/2402.18198 (2024) - [i20]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR abs/2405.02200 (2024) - [i19]Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier:
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. CoRR abs/2406.17322 (2024) - 2023
- [j10]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) - [j9]Tanja Tornede
PyExperimenter: Easily distribute experiments and track results. J. Open Source Softw. 8(86): 5149 (2023) - [j8]Felix Mohr
Naive automated machine learning. Mach. Learn. 112(4): 1131-1170 (2023) - [j7]Alexander Tornede
Algorithm selection on a meta level. Mach. Learn. 112(4): 1253-1286 (2023) - [c16]Marcel Wever
Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. GECCO 2023: 597-605 - [c15]David Schubert, Pritha Gupta, Marcel Wever:
Meta-learning for Automated Selection of Anomaly Detectors for Semi-supervised Datasets. IDA 2023: 392-405 - [c14]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). IJCAI 2023: 6964-6968 - [i18]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) - [i17]Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier:
Iterative Deepening Hyperband. CoRR abs/2302.00511 (2023) - 2022
- [b1]Marcel Wever:
Automated machine learning for multi-label classification. University of Paderborn, Germany, 2022 - [j6]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration. J. Artif. Intell. Res. 75: 425-487 (2022) - [j5]Eyke Hüllermeier
A flexible class of dependence-aware multi-label loss functions. Mach. Learn. 111(2): 713-737 (2022) - [i16]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration. CoRR abs/2202.01651 (2022) - [i15]Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman:
Hyperparameter optimization in deep multi-target prediction. CoRR abs/2211.04362 (2022) - [i14]David Schubert, Pritha Gupta, Marcel Wever:
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets. CoRR abs/2211.13681 (2022) - 2021
- [j4]Marcel Wever
AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3037-3054 (2021) - [j3]Felix Mohr
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3055-3066 (2021) - [c13]Tanja Tornede
Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. GECCO 2021: 368-376 - [c12]Jonas Hanselle
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. PAKDD (1) 2021: 152-163 - [i13]Felix Mohr, Marcel Wever:
Naive Automated Machine Learning - A Late Baseline for AutoML. CoRR abs/2103.10496 (2021) - [i12]Marie-Luis Merten, Marcel Wever, Michaela Geierhos, Doris Tophinke, Eyke Hüllermeier:
Annotation Uncertainty in the Context of Grammatical Change. CoRR abs/2105.07270 (2021) - [i11]Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier:
Algorithm Selection on a Meta Level. CoRR abs/2107.09414 (2021) - [i10]Eyke Hüllermeier, Felix Mohr, Alexander Tornede, Marcel Wever:
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. CoRR abs/2109.04744 (2021) - [i9]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) - [i8]Felix Mohr, Marcel Wever:
Naive Automated Machine Learning. CoRR abs/2111.14514 (2021) - 2020
- [j2]Marcel Wever
Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evol. Comput. 28(2): 165-193 (2020) - [c11]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 - [c10]Alexander Tornede
Extreme Algorithm Selection with Dyadic Feature Representation. DS 2020: 309-324 - [c9]Marcel Wever
LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. IDA 2020: 561-573 - [c8]Jonas Hanselle
Hybrid Ranking and Regression for Algorithm Selection. KI 2020: 59-72 - [c7]Tanja Tornede
AutoML for Predictive Maintenance: One Tool to RUL Them All. IoT Streams/ITEM@PKDD/ECML 2020: 106-118 - [i7]Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Extreme Algorithm Selection With Dyadic Feature Representation. CoRR abs/2001.10741 (2020) - [i6]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) - [i5]Stefan Heid, Marcel Wever, Eyke Hüllermeier:
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. CoRR abs/2008.01377 (2020) - [i4]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp
A Flexible Class of Dependence-aware Multi-Label Loss Functions. CoRR abs/2011.00792 (2020) - [i3]Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Towards Meta-Algorithm Selection. CoRR abs/2011.08784 (2020)
2010 – 2019
- 2019
- [c6]Felix Mohr, Marcel Wever
From Automated to On-The-Fly Machine Learning. GI-Jahrestagung 2019: 273-274 - 2018
- [j1]Felix Mohr, Marcel Wever
ML-Plan: Automated machine learning via hierarchical planning. Mach. Learn. 107(8-10): 1495-1515 (2018) - [c5]Felix Mohr, Marcel Wever
On-the-Fly Service Construction with Prototypes. SCC 2018: 225-232 - [c4]Felix Mohr, Marcel Wever
(WIP) Towards the Automated Composition of Machine Learning Services. SCC 2018: 241-244 - [c3]Marcel Wever
Ensembles of evolved nested dichotomies for classification. GECCO 2018: 561-568 - [c2]Felix Mohr, Marcel Wever
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
- [c1]Marcel Wever
Active coevolutionary learning of requirements specifications from examples. GECCO 2017: 1327-1334
Coauthor Index
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last updated on 2024-11-19 20:48 CET by the dblp team
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