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Veröffentlichungsdatum
Anzahl der Publikationen: 18
2024
Iliadis, Dimitrios 
ORCID: https://orcid.org/0000-0002-3676-5940; Wever, Marcel ORCID: https://orcid.org/0000-0001-9782-6818; De Baets, Bernard ORCID: https://orcid.org/0000-0002-3876-620X und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(November 2024):
Hyperparameter optimization of two-branch neural networks in multi-target prediction.
In: Applied Soft Computing, Bd. 165: S. 111957
[PDF, 849kB]
ORCID: https://orcid.org/0000-0002-3676-5940; Wever, Marcel ORCID: https://orcid.org/0000-0001-9782-6818; De Baets, Bernard ORCID: https://orcid.org/0000-0002-3876-620X und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(November 2024):
Hyperparameter optimization of two-branch neural networks in multi-target prediction.
In: Applied Soft Computing, Bd. 165: S. 111957
[PDF, 849kB]
Juergens, Mira; Meinert, Nis; Bengs, Viktor ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem
(Juli 2024):
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024.
In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research
Bd. 235
PMLR. S. 22624-22642
[PDF, 8MB]
ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem
(Juli 2024):
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024.
In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research
Bd. 235
PMLR. S. 22624-22642
[PDF, 8MB]
2023
Mortier, Thomas ORCID: https://orcid.org/0000-0001-9650-9263; Bengs, Viktor ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Luca, Stijn und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(April 2023):
On the Calibration of Probabilistic Classifier Sets.
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Valencia, Spain, 25-27 April, 2023.
Ruiz, Francisco; Dy, Jennifer und van de Meent, Jan-Willem (Hrsg.):
In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics,
Bd. 206
PMLR. S. 8857-8870
[PDF, 764kB]
ORCID: https://orcid.org/0000-0001-9650-9263; Bengs, Viktor ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Luca, Stijn und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(April 2023):
On the Calibration of Probabilistic Classifier Sets.
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Valencia, Spain, 25-27 April, 2023.
Ruiz, Francisco; Dy, Jennifer und van de Meent, Jan-Willem (Hrsg.):
In: Proceedings of The 26th International Conference on Artificial Intelligence and Statistics,
Bd. 206
PMLR. S. 8857-8870
[PDF, 764kB]
Bengs, Viktor ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(2023):
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification.
40th International Conference on Machine Learning (ICML 2023), Hawaii, USA, 23-29 July, 2023.
Krause, Andreas; Brunskill, Emma; Cho, Kyunghyun; Engelhardt, Barbara; Sabato, Sivan und Scarlett, Jonathan (Hrsg.):
In: Proceedings of the 40th International Conference on Machine Learning,
Bd. 202
PMLR. S. 2078-2091
[PDF, 400kB]
ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(2023):
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification.
40th International Conference on Machine Learning (ICML 2023), Hawaii, USA, 23-29 July, 2023.
Krause, Andreas; Brunskill, Emma; Cho, Kyunghyun; Engelhardt, Barbara; Sabato, Sivan und Scarlett, Jonathan (Hrsg.):
In: Proceedings of the 40th International Conference on Machine Learning,
Bd. 202
PMLR. S. 2078-2091
[PDF, 400kB]
2022
Bengs, Viktor ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(28. November 2022):
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation.
Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, 28 November - 9 December 2022.
[PDF, 385kB]
ORCID: https://orcid.org/0000-0001-6988-6186; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(28. November 2022):
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation.
Thirty-eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, USA, 28 November - 9 December 2022.
[PDF, 385kB]
Mortier, Thomas ORCID: https://orcid.org/0000-0001-9650-9263; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Dembczyński, Krzysztof und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(August 2022):
Set-valued prediction in hierarchical classification with constrained representation complexity.
38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022.
Cussens, James und Zhang, Kun (Hrsg.):
In: Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence,
Bd. 180
PMLR. S. 1392-1401
[PDF, 600kB]
ORCID: https://orcid.org/0000-0001-9650-9263; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Dembczyński, Krzysztof und Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003
(August 2022):
Set-valued prediction in hierarchical classification with constrained representation complexity.
38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022.
Cussens, James und Zhang, Kun (Hrsg.):
In: Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence,
Bd. 180
PMLR. S. 1392-1401
[PDF, 600kB]
2021
Mortier, Thomas ORCID: https://orcid.org/0000-0001-9650-9263; Wydmuch, Marek; Dembczyński, Krzysztof; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem
(Mai 2021):
Efficient set-valued prediction in multi-class classification.
In: Data Mining and Knowledge Discovery, Bd. 35, Nr. 4: S. 1435-1469
[PDF, 815kB]
ORCID: https://orcid.org/0000-0001-9650-9263; Wydmuch, Marek; Dembczyński, Krzysztof; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem
(Mai 2021):
Efficient set-valued prediction in multi-class classification.
In: Data Mining and Knowledge Discovery, Bd. 35, Nr. 4: S. 1435-1469
[PDF, 815kB]
Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem
(März 2021):
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods.
In: Machine Learning, Nr. 3: S. 457-506
[PDF, 4MB]
ORCID: https://orcid.org/0000-0002-9944-4108 und Waegeman, Willem
(März 2021):
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods.
In: Machine Learning, Nr. 3: S. 457-506
[PDF, 4MB]
Mortier, Thomas; Wydmuch, Marek; Dembczynski, Krzysztof; Hüllermeier, Eyke und Waegeman, Willem
(2021):
Efficient set-valued prediction in multi-class classification.
In: Data Mining and Knowledge Discovery, Bd. 35, Nr. 4: S. 1435-1469
2019
Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003; Dembczyński, Krzysztof und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(2019):
Multi-target prediction: a unifying view on problems and methods.
In: Data Mining and Knowledge Discovery, Bd. 33, Nr. 2: S. 293-324
ORCID: https://orcid.org/0000-0002-5950-3003; Dembczyński, Krzysztof und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(2019):
Multi-target prediction: a unifying view on problems and methods.
In: Data Mining and Knowledge Discovery, Bd. 33, Nr. 2: S. 293-324
2016
Dembczyński, Krzysztof; Kotłowski, Wojciech; Waegeman, Willem; Busa-Fekete, Róbert und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(2016):
Consistency of Probabilistic Classifier Trees.
In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe und Vreeken, Jilles (Hrsg.):
Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 9852. Cham: Springer. S. 511-526
[PDF, 322kB]
ORCID: https://orcid.org/0000-0002-9944-4108
(2016):
Consistency of Probabilistic Classifier Trees.
In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe und Vreeken, Jilles (Hrsg.):
Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II. Lecture Notes in Computer Science, Bd. 9852. Cham: Springer. S. 511-526
[PDF, 322kB]
2014
Waegeman, Willem ORCID: https://orcid.org/0000-0002-5950-3003; Dembczyński, Krzysztof; Jachnik, Arkadiusz; Cheng, Weiwei und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(November 2014):
On the Bayes-Optimality of F-Measure Maximizers.
In: Journal of Machine Learning Research, Bd. 15, 103: S. 3513-3568
[PDF, 676kB]
ORCID: https://orcid.org/0000-0002-5950-3003; Dembczyński, Krzysztof; Jachnik, Arkadiusz; Cheng, Weiwei und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(November 2014):
On the Bayes-Optimality of F-Measure Maximizers.
In: Journal of Machine Learning Research, Bd. 15, 103: S. 3513-3568
[PDF, 676kB]
Stock, Michiel; Fober, Thomas; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Glinca, Serghei; Klebe, Gerhard; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard und Waegeman, Willem
(2014):
Identification of Functionally Related Enzymes by Learning-to-Rank Methods.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bd. 11, Nr. 6: S. 1157-1169
ORCID: https://orcid.org/0000-0002-9944-4108; Glinca, Serghei; Klebe, Gerhard; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard und Waegeman, Willem
(2014):
Identification of Functionally Related Enzymes by Learning-to-Rank Methods.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Bd. 11, Nr. 6: S. 1157-1169
2013
Dembczyński, Krzysztof; Jachnik, Arkadiusz; Kotlowski, Wojciech; Waegeman, Willem und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(2013):
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.
ICML'13: 30th International Conference on International Conference on Machine Learning, Atlanta GA USA, June 16 - 21, 2013.
Dasgupta, Sanjoy und McAllester, David (Hrsg.):
In: Proceedings of the 30th International Conference on Machine Learning,
Bd. 28, Nr. 3
S. 1030-1038
[PDF, 378kB]
ORCID: https://orcid.org/0000-0002-9944-4108
(2013):
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.
ICML'13: 30th International Conference on International Conference on Machine Learning, Atlanta GA USA, June 16 - 21, 2013.
Dasgupta, Sanjoy und McAllester, David (Hrsg.):
In: Proceedings of the 30th International Conference on Machine Learning,
Bd. 28, Nr. 3
S. 1030-1038
[PDF, 378kB]
2012
Dembczyński, Krzysztof; Waegeman, Willem und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(August 2012):
An Analysis of Chaining in Multi-Label Classification.
In: De Raedt, Luc; Bessiere, Christian; Dubois, Didier; Doherty, Patrick; Frasconi, Paolo; Heintz, Fredrik und Lucas, Peter (Hrsg.):
ECAI 2012 : 20th European Conference on Artificial Intelligence, 27 - 31 August 2012, Montpellier, France. Frontiers in Artificial Intelligence and Applications, Bd. 242. Amsterdam: IOS Press. S. 294-299
[PDF, 258kB]
ORCID: https://orcid.org/0000-0002-9944-4108
(August 2012):
An Analysis of Chaining in Multi-Label Classification.
In: De Raedt, Luc; Bessiere, Christian; Dubois, Didier; Doherty, Patrick; Frasconi, Paolo; Heintz, Fredrik und Lucas, Peter (Hrsg.):
ECAI 2012 : 20th European Conference on Artificial Intelligence, 27 - 31 August 2012, Montpellier, France. Frontiers in Artificial Intelligence and Applications, Bd. 242. Amsterdam: IOS Press. S. 294-299
[PDF, 258kB]
Cheng, Weiwei; Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108; Waegeman, Willem und Welker, Volkmar
(2012):
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models.
25. NIPS 2012, Lake Tahoe, Nevada, USA, December 3-8, 2012.
Advances in Neural Information Processing Systems.
Bd. 25
S. 2510-2518
[PDF, 252kB]
ORCID: https://orcid.org/0000-0002-9944-4108; Waegeman, Willem und Welker, Volkmar
(2012):
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models.
25. NIPS 2012, Lake Tahoe, Nevada, USA, December 3-8, 2012.
Advances in Neural Information Processing Systems.
Bd. 25
S. 2510-2518
[PDF, 252kB]
Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(2012):
On label dependence and loss minimization in multi-label classification.
In: Machine Learning, Bd. 88, Nr. 1-2: S. 5-45
[PDF, 1MB]
ORCID: https://orcid.org/0000-0002-9944-4108
(2012):
On label dependence and loss minimization in multi-label classification.
In: Machine Learning, Bd. 88, Nr. 1-2: S. 5-45
[PDF, 1MB]
2010
Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei und Hüllermeier, Eyke ORCID: https://orcid.org/0000-0002-9944-4108
(2010):
Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss.
In: Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides und Sebag, Michèle (Hrsg.):
Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 6321. Berlin, Heidelberg: Springer. S. 280-295
ORCID: https://orcid.org/0000-0002-9944-4108
(2010):
Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss.
In: Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides und Sebag, Michèle (Hrsg.):
Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science, Bd. 6321. Berlin, Heidelberg: Springer. S. 280-295