| 2024 | ICML | Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? | Mira Jrgens, Nis Meinert, Viktor Bengs, Eyke Hllermeier, Willem Waegeman |
| 2023 | AISTATS | On the Calibration of Probabilistic Classifier Sets. | Thomas Mortier, Viktor Bengs, Eyke Hllermeier, Stijn Luca, Willem Waegeman |
| 2023 | ICML | On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. | Viktor Bengs, Eyke Hllermeier, Willem Waegeman |
| 2022 | UAI | Set-valued prediction in hierarchical classification with constrained representation complexity. | Thomas Mortier, Eyke Hllermeier, Krzysztof Dembczynski, Willem Waegeman |
| 2013 | ICML | Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. | Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hllermeier |
| 2012 | ECAI | An Analysis of Chaining in Multi-Label Classification. | Krzysztof Dembczynski, Willem Waegeman, Eyke Hllermeier |
| 2011 | ISDA | ERA ranking representability: The missing link between ordinal regression and multi-class classification. | Willem Waegeman, Bernard De Baets |
| 2010 | ESANN | Directional predictions for 4-class BCI data. | Dieter Devlaminck, Willem Waegeman, Bruno Bauwens, Bart Wyns, Georges Otte, Luc Boullart, Patrick Santens |