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Willem Waegeman
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Publications
- 2024
- [i21]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR abs/2402.09056 (2024) - 2023
- [c27]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On the Calibration of Probabilistic Classifier Sets. AISTATS 2023: 8857-8870 - [c26]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML 2023: 2078-2091 - [i20]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR abs/2301.12736 (2023) - 2022
- [c25]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS 2022 - [c24]Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. UAI 2022: 1392-1401 - [i18]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. CoRR abs/2203.06102 (2022) - [i17]Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. CoRR abs/2203.06676 (2022) - [i16]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On Calibration of Ensemble-Based Credal Predictors. CoRR abs/2205.10082 (2022) - 2021
- [j28]Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman:
Efficient set-valued prediction in multi-class classification. Data Min. Knowl. Discov. 35(4): 1435-1469 (2021) - [j27]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3): 457-506 (2021) - 2019
- [j24]Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier:
Multi-target prediction: a unifying view on problems and methods. Data Min. Knowl. Discov. 33(2): 293-324 (2019) - [c23]Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman:
Set-Valued Prediction in Multi-Class Classification. BNAIC/BENELEARN 2019 - [i12]Thomas Mortier, Marek Wydmuch, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. CoRR abs/1906.08129 (2019) - [i11]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. CoRR abs/1910.09457 (2019) - 2018
- [i9]Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier:
Multi-Target Prediction: A Unifying View on Problems and Methods. CoRR abs/1809.02352 (2018) - 2016
- [c16]Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier:
Consistency of Probabilistic Classifier Trees. ECML/PKDD (2) 2016: 511-526 - 2014
- [j19]Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier:
On the bayes-optimality of F-measure maximizers. J. Mach. Learn. Res. 15(1): 3333-3388 (2014) - [j18]Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Identification of Functionally Related Enzymes by Learning-to-Rank Methods. IEEE ACM Trans. Comput. Biol. Bioinform. 11(6): 1157-1169 (2014) - [i5]Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Identification of functionally related enzymes by learning-to-rank methods. CoRR abs/1405.4394 (2014) - 2013
- [c14]Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hüllermeier:
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. ICML (3) 2013: 1130-1138 - [i3]Willem Waegeman, Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier:
On the Bayes-optimality of F-measure maximizers. CoRR abs/1310.4849 (2013) - 2012
- [j14]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
On label dependence and loss minimization in multi-label classification. Mach. Learn. 88(1-2): 5-45 (2012) - [c13]Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier:
An Analysis of Chaining in Multi-Label Classification. ECAI 2012: 294-299 - [c12]Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker:
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. NIPS 2012: 2510-2518 - [c11]Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier, Adrian Jaroszewicz, Willem Waegeman:
F-Measure Maximization in Topical Classification. RSCTC 2012: 439-446 - 2011
- [c6]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
An Exact Algorithm for F-Measure Maximization. NIPS 2011: 1404-1412 - 2010
- [c3]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss. ECML/PKDD (1) 2010: 280-295
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