Willem Waegeman

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 45

Top Topics

Support Vector Machine

Machine Learning

Relational Models

Prediction Intervals

Top Venues

Data Min. Knowl. Discov.

Publications

Nina Schmid, David Fernandes del Pozo, Willem Waegeman, Jan Hasenauer
Assessment of Uncertainty Quantification in Universal Differential Equations. CoRR (2024)
Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman
Hyperparameter optimization of two-branch neural networks in multi-target prediction. Appl. Soft Comput. 165 (2024)
Dimitrios Iliadis, Bernard De Baets, Tapio Pahikkala, Willem Waegeman
A comparison of embedding aggregation strategies in drug-target interaction prediction. BMC Bioinform. 25 (1) (2024)
Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR (2024)
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
On the Calibration of Probabilistic Classifier Sets. AISTATS (2023)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR (2023)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML (2023)
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Valid prediction intervals for regression problems. Artif. Intell. Rev. 56 (1) (2023)
Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
DeepMTP: A Python-based deep learning framework for multi-target prediction. SoftwareX 23 (2023)
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Heteroskedastic conformal regression. CoRR (2023)
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 (2022)
Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
Multi-target prediction for dummies using two-branch neural networks. Mach. Learn. 111 (2) (2022)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS (2022)
Gaetan De Waele, Jim Clauwaert, Gerben Menschaert, Willem Waegeman
CpG Transformer for imputation of single-cell methylomes. Bioinform. 38 (3) (2022)
Jim Clauwaert, Willem Waegeman
Novel Transformer Networks for Improved Sequence Labeling in genomics. IEEE ACM Trans. Comput. Biol. Bioinform. 19 (1) (2022)
Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman
Hyperparameter optimization in deep multi-target prediction. CoRR (2022)
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
On Calibration of Ensemble-Based Credal Predictors. CoRR (2022)
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
Set-valued prediction in hierarchical classification with constrained representation complexity. UAI (2022)
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
Set-valued prediction in hierarchical classification with constrained representation complexity. CoRR (2022)
Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
Automated problem setting selection in multi-target prediction with AutoMTP. CoRR (2021)
Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman
Efficient set-valued prediction in multi-class classification. Data Min. Knowl. Discov. 35 (4) (2021)
Jim Clauwaert, Gerben Menschaert, Willem Waegeman
Explainability in transformer models for functional genomics. Briefings Bioinform. 22 (5) (2021)
Nicolas Dewolf, Bernard De Baets, Willem Waegeman
Well-calibrated prediction intervals for regression problems. CoRR (2021)
Eyke Hüllermeier, Willem Waegeman
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110 (3) (2021)
Michiel Stock, Tapio Pahikkala, Antti Airola, Willem Waegeman, Bernard De Baets
Algebraic shortcuts for leave-one-out cross-validation in supervised network inference. Briefings Bioinform. 21 (1) (2020)
Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman
Set-Valued Prediction in Multi-Class Classification. BNAIC/BENELEARN (2019)
Christina Papagiannopoulou, René Parchen, Willem Waegeman
Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data. ECML/PKDD (3) (2019)
Thomas Mortier, Marek Wydmuch, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. CoRR (2019)
Eyke Hüllermeier, Willem Waegeman
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. CoRR (2019)
Mieke Deschepper, Kristof Eeckloo, Dirk Vogelaers, Willem Waegeman
A hospital wide predictive model for unplanned readmission using hierarchical ICD data. Comput. Methods Programs Biomed. 173 (2019)
Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier
Multi-target prediction: a unifying view on problems and methods. Data Min. Knowl. Discov. 33 (2) (2019)
Christina Papagiannopoulou, René Parchen, Willem Waegeman
Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data. BNAIC/BENELEARN (2019)
Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression. CoRR (2018)
Stijn Decubber, Thomas Mortier, Krzysztof Dembczynski, Willem Waegeman
Deep F-Measure Maximization in Multi-label Classification: A Comparative Study. ECML/PKDD (1) (2018)
Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression. Neural Comput. 30 (8) (2018)
Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier
Multi-Target Prediction: A Unifying View on Problems and Methods. CoRR (2018)
Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, Christina Papagiannopoulou, Stijn Decubber, Willem Waegeman
A non-linear data-driven approach to reveal global vegetation sensitivity to climate. MultiTemp (2017)
Brecht Martens, Diego G. Miralles, Wouter A. Dorigo, Willem Waegeman, Niko E. C. Verhoest
Investigating the control of ocean-atmospheric oscillations over global terrestrial evaporation using a simple supervised learning method. MultiTemp (2017)
Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman
Analyzing Granger Causality in Climate Data with Time Series Classification Methods. ECML/PKDD (3) (2017)
Michiel Stock, Krzysztof Dembczynski, Bernard De Baets, Willem Waegeman
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models. Data Min. Knowl. Discov. 30 (5) (2016)
Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier
Consistency of Probabilistic Classifier Trees. ECML/PKDD (2) (2016)
Michiel Stock, Krzysztof Dembczynski, Bernard De Baets, Willem Waegeman
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models. CoRR (2016)
Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method. CoRR (2016)
Tapio Pahikkala, Markus Viljanen, Antti Airola, Willem Waegeman
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels. CoRR (2015)
E. Colman, Willem Waegeman, Bernard De Baets, Veerle Fievez
Prediction of subacute ruminal acidosis based on milk fatty acids: A comparison of linear discriminant and support vector machine approaches for model development. Comput. Electron. Agric. 111 (2015)
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) (2014)
Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier
On the bayes-optimality of F-measure maximizers. J. Mach. Learn. Res. 15 (1) (2014)
Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman
A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction. ECML/PKDD (2) (2014)
Jan Peters, Willem Waegeman, Jasper Van doninck, Els I. Ducheyne, Carlos Calvete, J. Lucientes, Niko E. C. Verhoest, Bernard De Baets
Predicting spatio-temporal Culicoides imicola distributions in Spain based on environmental habitat characteristics and species dispersal. Ecol. Informatics 22 (2014)
Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman
A two-step learning approach for solving full and almost full cold start problems in dyadic prediction. CoRR (2014)
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 (2014)
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)
Willem Waegeman, Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier
On the Bayes-optimality of F-measure maximizers. CoRR (2013)
Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman
Efficient regularized least-squares algorithms for conditional ranking on relational data. Mach. Learn. 93 (2-3) (2013)
Shinji Fukuda, Bernard De Baets, Willem Waegeman, Jan Verwaeren, Ans M. Mouton
Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ. Model. Softw. 47 (2013)
Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier, Adrian Jaroszewicz, Willem Waegeman
F-Measure Maximization in Topical Classification. RSCTC (2012)
Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets
A Kernel-Based Framework for Learning Graded Relations From Data. IEEE Trans. Fuzzy Syst. 20 (6) (2012)
Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier
An Analysis of Chaining in Multi-Label Classification. ECAI (2012)
Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
On label dependence and loss minimization in multi-label classification. Mach. Learn. 88 (1-2) (2012)
Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data CoRR (2012)
Jan Verwaeren, Willem Waegeman, Bernard De Baets
Learning partial ordinal class memberships with kernel-based proportional odds models. Comput. Stat. Data Anal. 56 (4) (2012)
Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. NIPS (2012)
Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets
A kernel-based framework for learning graded relations from data CoRR (2011)
Willem Waegeman, Bernard De Baets
ERA ranking representability: The missing link between ordinal regression and multi-class classification. ISDA (2011)
Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets
Learning Valued Relations from Data. Eurofuse (2011)
Shinji Fukuda, Jun Nakajima, Bernard De Baets, Willem Waegeman, Takahiko Mukai, Ans M. Mouton, Norio Onikura
A discussion on the accuracy-complexity relationship in modelling fish habitat preference using genetic Takagi-Sugeno fuzzy systems. GEFS (2011)
Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski
An experimental comparison of cross-validation techniques for estimating the area under the ROC curve. Comput. Stat. Data Anal. 55 (4) (2011)
Shinji Fukuda, Willem Waegeman, Ans M. Mouton, Bernard De Baets
Modelling Fish Habitat Preference with a Genetic Algorithm-Optimized Takagi-Sugeno Model Based on Pairwise Comparisons. Eurofuse (2011)
Willem Waegeman, Jan Verwaeren, Bram Slabbinck, Bernard De Baets
Supervised learning algorithms for multi-class classification problems with partial class memberships. Fuzzy Sets Syst. 184 (1) (2011)
Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
An Exact Algorithm for F-Measure Maximization. NIPS (2011)
Willem Waegeman, Bernard De Baets
On the ERA ranking representability of pairwise bipartite ranking functions. Artif. Intell. 175 (7-8) (2011)
Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski
A comparison of AUC estimators in small-sample studies. MLSB (2010)
Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Paul De Vos, Bernard De Baets
From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification. BMC Bioinform. 11 (2010)
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)
Shinji Fukuda, Norio Onikura, Bernard De Baets, Willem Waegeman, Ans M. Mouton, Jun Nakajima, Takahiko Mukai
A genetic Takagi-Sugeno fuzzy system for fish habitat preference modelling. NaBIC (2010)
Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, Tapio Salakoski, Bernard De Baets
Learning intransitive reciprocal relations with kernel methods. Eur. J. Oper. Res. 206 (3) (2010)
Willem Waegeman, Bernard De Baets
A transitivity analysis of bipartite rankings in pairwise multi-class classification. Inf. Sci. 180 (21) (2010)
Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski, Bernard De Baets
Conditional Ranking on Relational Data. ECML/PKDD (2) (2010)
Dieter Devlaminck, Willem Waegeman, Bruno Bauwens, Bart Wyns, Georges Otte, Luc Boullart, Patrick Santens
Directional predictions for 4-class BCI data. ESANN (2010)
Willem Waegeman, Bernard De Baets, Luc Boullart
Kernel-based learning methods for preference aggregation. 4OR 7 (2) (2009)
Willem Waegeman
Learning to rank: a ROC-based graph-theoretic approach. 4OR 7 (4) (2009)
Willem Waegeman, Bernard De Baets, Luc Boullart
ROC analysis in ordinal regression learning. Pattern Recognit. Lett. 29 (1) (2008)
Willem Waegeman, Bernard De Baets, Luc Boullart
On the scalability of ordered multi-class ROC analysis. Comput. Stat. Data Anal. 52 (7) (2008)
Willem Waegeman, Johannes Cottyn, Bart Wyns, Luc Boullart, Bernard De Baets, Lieva Van Langenhove, Jan Detand
Classifying carpets based on laser scanner data. Eng. Appl. Artif. Intell. 21 (6) (2008)
Willem Waegeman, Bernard De Baets, Luc Boullart
Learning layered ranking functions with structured support vector machines. Neural Networks 21 (10) (2008)