Waegeman, Willem

21 publications

MLJ 2025 A Calibration Test for Evaluating Set-Based Epistemic Uncertainty Representations Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman

ICML 2024 Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? Mira Juergens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

ICML 2023 On Second-Order Scoring Rules for Epistemic Uncertainty Quantification Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

AISTATS 2023 On the Calibration of Probabilistic Classifier Sets Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

MLJ 2022 Multi-Target Prediction for Dummies Using Two-Branch Neural Networks Dimitrios Iliadis, Bernard De Baets, Willem Waegeman

NeurIPS 2022 Pitfalls of Epistemic Uncertainty Quantification Through Loss Minimisation Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

UAI 2022 Set-Valued Prediction in Hierarchical Classification with Constrained Representation Complexity Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman

MLJ 2021 Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier, Willem Waegeman

ECML-PKDD 2019 Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data Christina Papagiannopoulou, René Parchen, Willem Waegeman

ECML-PKDD 2018 Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study Stijn Decubber, Thomas Mortier, Krzysztof Dembczynski, Willem Waegeman

ECML-PKDD 2017 Analyzing Granger Causality in Climate Data with Time Series Classification Methods Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman

ECML-PKDD 2016 Consistency of Probabilistic Classifier Trees Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier

ECML-PKDD 2014 A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman

JMLR 2014 On the Bayes-Optimality of F-Measure Maximizers Willem Waegeman, Krzysztof Dembczyński, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier

MLJ 2013 Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman

ICML 2013 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 Huellermeier

NeurIPS 2012 Label Ranking with Partial Abstention Based on Thresholded Probabilistic Models Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker

MLJ 2012 On Label Dependence and Loss Minimization in Multi-Label Classification Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

NeurIPS 2011 An Exact Algorithm for F-Measure Maximization Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier

ECML-PKDD 2010 Conditional Ranking on Relational Data Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski, Bernard De Baets

ECML-PKDD 2010 Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier