Willem Waegeman

103 papers A* 3A 3B 1C 1Journal 73Unranked 21

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	How negative sampling shapes the performance of transcription factor binding site prediction models. Bioinform. Natan Tourne, Gaetan De Waele, Vanessa Vermeirssen, Willem Waegeman
2026	J	`jnl`	Small molecule retrieval from tandem mass spectrometry: what are we optimizing for? CoRR Gaetan De Waele, Marek Wydmuch, Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman
2026	J	`jnl`	When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra. CoRR Mira Jürgens, Gaetan De Waele, Morteza Rakhshaninejad, Willem Waegeman
2025	J	`jnl`	A calibration test for evaluating set-based epistemic uncertainty representations. CoRR Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman
2025	J	`jnl`	A calibration test for evaluating set-based epistemic uncertainty representations. Mach. Learn. Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman
2025	—	`conf`	Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction. COPA Morteza Rakhshaninejad, Mira Jürgens, Nicolas Dewolf, Willem Waegeman
2025	J	`jnl`	Conformal Prediction for Uncertainty Estimation in Drug-Target Interaction Prediction. CoRR Morteza Rakhshaninejad, Mira Jürgens, Nicolas Dewolf, Willem Waegeman
2025	J	`jnl`	Conformal Prediction in Hierarchical Classification. CoRR Thomas Mortier, Alireza Javanmardi, Yusuf Sale, Eyke Hüllermeier, Willem Waegeman
2025	J	`jnl`	Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks. J. Chem. Inf. Model. Regina Ibragimova, Dimitrios Iliadis, Willem Waegeman
2025	J	`jnl`	Evaluation of out-of-distribution detection methods for data shifts in single-cell transcriptomics. Briefings Bioinform. Lauren Theunissen, Thomas Mortier, Yvan Saeys, Willem Waegeman
2025	J	`jnl`	Optimal Conformal Prediction under Epistemic Uncertainty. CoRR Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies, Willem Waegeman, Aleksandar Bojchevski, Eyke Hüllermeier
2025	J	`jnl`	Pre-trained Maldi Transformers improve MALDI-TOF MS-based prediction. Comput. Biol. Medicine Gaetan De Waele, Gerben Menschaert, Peter Vandamme, Willem Waegeman
2025	J	`jnl`	Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition. CoRR Arthur Hoarau, Benjamin Quost, Sébastien Destercke, Willem Waegeman
2025	J	`jnl`	Why Machine Learning Models Fail to Fully Capture Epistemic Uncertainty. CoRR Sebastián Jiménez, Mira Jürgens, Willem Waegeman
2024	J	`jnl`	A comparison of embedding aggregation strategies in drug-target interaction prediction. BMC Bioinform. Dimitrios Iliadis, Bernard De Baets, Tapio Pahikkala, Willem Waegeman
2024	J	`jnl`	Assessment of Uncertainty Quantification in Universal Differential Equations. CoRR Nina Schmid, David Fernandes del Pozo, Willem Waegeman, Jan Hasenauer
2024	J	`jnl`	Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks. CoRR Regina Ibragimova, Dimitrios Iliadis, Willem Waegeman
2024	J	`jnl`	Hyperparameter optimization of two-branch neural networks in multi-target prediction. Appl. Soft Comput. Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman
2024	A*	`conf`	Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? ICML Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2024	J	`jnl`	Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2024	J	`jnl`	Uncertainty-aware single-cell annotation with a hierarchical reject option. Bioinform. Lauren Theunissen, Thomas Mortier, Yvan Saeys, Willem Waegeman
2023	J	`jnl`	DeepMTP: A Python-based deep learning framework for multi-target prediction. SoftwareX Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
2023	J	`jnl`	Heteroskedastic conformal regression. CoRR Nicolas Dewolf, Bernard De Baets, Willem Waegeman
2023	A*	`conf`	On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2023	J	`jnl`	On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2023	A	`conf`	On the Calibration of Probabilistic Classifier Sets. AISTATS Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
2023	J	`jnl`	Valid prediction intervals for regression problems. Artif. Intell. Rev. Nicolas Dewolf, Bernard De Baets, Willem Waegeman
2022	J	`jnl`	CpG Transformer for imputation of single-cell methylomes. Bioinform. Gaetan De Waele, Jim Clauwaert, Gerben Menschaert, Willem Waegeman
2022	J	`jnl`	Hyperparameter optimization in deep multi-target prediction. CoRR Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman
2022	J	`jnl`	Multi-target prediction for dummies using two-branch neural networks. Mach. Learn. Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
2022	J	`jnl`	Novel Transformer Networks for Improved Sequence Labeling in genomics. IEEE ACM Trans. Comput. Biol. Bioinform. Jim Clauwaert, Willem Waegeman
2022	J	`jnl`	On Calibration of Ensemble-Based Credal Predictors. CoRR Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
2022	J	`jnl`	On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. CoRR Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2022	A*	`conf`	Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
2022	A	`conf`	Set-valued prediction in hierarchical classification with constrained representation complexity. UAI Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
2022	J	`jnl`	Set-valued prediction in hierarchical classification with constrained representation complexity. CoRR Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
2021	J	`jnl`	Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. Eyke Hüllermeier, Willem Waegeman
2021	J	`jnl`	Automated problem setting selection in multi-target prediction with AutoMTP. CoRR Dimitrios Iliadis, Bernard De Baets, Willem Waegeman
2021	J	`jnl`	Efficient set-valued prediction in multi-class classification. Data Min. Knowl. Discov. Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman
2021	J	`jnl`	Explainability in transformer models for functional genomics. Briefings Bioinform. Jim Clauwaert, Gerben Menschaert, Willem Waegeman
2021	J	`jnl`	Well-calibrated prediction intervals for regression problems. CoRR Nicolas Dewolf, Bernard De Baets, Willem Waegeman
2020	J	`jnl`	Algebraic shortcuts for leave-one-out cross-validation in supervised network inference. Briefings Bioinform. Michiel Stock, Tapio Pahikkala, Antti Airola, Willem Waegeman, Bernard De Baets
2019	J	`jnl`	A hospital wide predictive model for unplanned readmission using hierarchical ICD data. Comput. Methods Programs Biomed. Mieke Deschepper, Kristof Eeckloo, Dirk Vogelaers, Willem Waegeman
2019	J	`jnl`	Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. CoRR Eyke Hüllermeier, Willem Waegeman
2019	J	`jnl`	Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. CoRR Thomas Mortier, Marek Wydmuch, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
2019	—	`conf`	Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data. BNAIC/BENELEARN Christina Papagiannopoulou, René Parchen, Willem Waegeman
2019	—	`conf`	Investigating Time Series Classification Techniques for Rapid Pathogen Identification with Single-Cell MALDI-TOF Mass Spectrum Data. ECML/PKDD (3) Christina Papagiannopoulou, René Parchen, Willem Waegeman
2019	J	`jnl`	Multi-target prediction: a unifying view on problems and methods. Data Min. Knowl. Discov. Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier
2019	—	`conf`	Set-Valued Prediction in Multi-Class Classification. BNAIC/BENELEARN Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman
2018	J	`jnl`	A Comparative Study of Pairwise Learning Methods Based on Kernel Ridge Regression. Neural Comput. Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
2018	J	`jnl`	A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression. CoRR Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
2018	—	`conf`	Deep F-Measure Maximization in Multi-label Classification: A Comparative Study. ECML/PKDD (1) Stijn Decubber, Thomas Mortier, Krzysztof Dembczynski, Willem Waegeman
2018	J	`jnl`	Multi-Target Prediction: A Unifying View on Problems and Methods. CoRR Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier
2017	—	`conf`	A non-linear data-driven approach to reveal global vegetation sensitivity to climate. MultiTemp Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Wouter A. Dorigo, Christina Papagiannopoulou, Stijn Decubber, Willem Waegeman
2017	—	`conf`	Analyzing Granger Causality in Climate Data with Time Series Classification Methods. ECML/PKDD (3) Christina Papagiannopoulou, Stijn Decubber, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, Willem Waegeman
2017	—	`conf`	Investigating the control of ocean-atmospheric oscillations over global terrestrial evaporation using a simple supervised learning method. MultiTemp Brecht Martens, Diego G. Miralles, Wouter A. Dorigo, Willem Waegeman, Niko E. C. Verhoest
2016	—	`conf`	Consistency of Probabilistic Classifier Trees. ECML/PKDD (2) Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier
2016	J	`jnl`	Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method. CoRR Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
2016	J	`jnl`	Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models. CoRR Michiel Stock, Krzysztof Dembczynski, Bernard De Baets, Willem Waegeman
2016	J	`jnl`	Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models. Data Min. Knowl. Discov. Michiel Stock, Krzysztof Dembczynski, Bernard De Baets, Willem Waegeman
2015	J	`jnl`	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. E. Colman, Willem Waegeman, Bernard De Baets, Veerle Fievez
2015	J	`jnl`	Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels. CoRR Tapio Pahikkala, Markus Viljanen, Antti Airola, Willem Waegeman
2014	—	`conf`	A Two-Step Learning Approach for Solving Full and Almost Full Cold Start Problems in Dyadic Prediction. ECML/PKDD (2) Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman
2014	J	`jnl`	A two-step learning approach for solving full and almost full cold start problems in dyadic prediction. CoRR Tapio Pahikkala, Michiel Stock, Antti Airola, Tero Aittokallio, Bernard De Baets, Willem Waegeman
2014	J	`jnl`	Identification of Functionally Related Enzymes by Learning-to-Rank Methods. IEEE ACM Trans. Comput. Biol. Bioinform. Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
2014	J	`jnl`	Identification of functionally related enzymes by learning-to-rank methods. CoRR Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman
2014	J	`jnl`	On the bayes-optimality of F-measure maximizers. J. Mach. Learn. Res. Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier
2014	J	`jnl`	Predicting spatio-temporal Culicoides imicola distributions in Spain based on environmental habitat characteristics and species dispersal. Ecol. Informatics Jan Peters, Willem Waegeman, Jasper Van doninck, Els I. Ducheyne, Carlos Calvete, J. Lucientes, Niko E. C. Verhoest, Bernard De Baets
2013	J	`jnl`	Efficient regularized least-squares algorithms for conditional ranking on relational data. Mach. Learn. Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman
2013	J	`jnl`	Habitat prediction and knowledge extraction for spawning European grayling (Thymallus thymallus L.) using a broad range of species distribution models. Environ. Model. Softw. Shinji Fukuda, Bernard De Baets, Willem Waegeman, Jan Verwaeren, Ans M. Mouton
2013	J	`jnl`	On the Bayes-optimality of F-measure maximizers. CoRR Willem Waegeman, Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier
2013	—	`conf`	Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. ICML (3) Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hüllermeier
2012	J	`jnl`	A Kernel-Based Framework for Learning Graded Relations From Data. IEEE Trans. Fuzzy Syst. Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets
2012	A	`conf`	An Analysis of Chaining in Multi-Label Classification. ECAI Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier
2012	J	`jnl`	Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data CoRR Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman
2012	—	`conf`	F-Measure Maximization in Topical Classification. RSCTC Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier, Adrian Jaroszewicz, Willem Waegeman
2012	—	`conf`	Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. NIPS Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker
2012	J	`jnl`	Learning partial ordinal class memberships with kernel-based proportional odds models. Comput. Stat. Data Anal. Jan Verwaeren, Willem Waegeman, Bernard De Baets
2012	J	`jnl`	On label dependence and loss minimization in multi-label classification. Mach. Learn. Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
2011	—	`conf`	A discussion on the accuracy-complexity relationship in modelling fish habitat preference using genetic Takagi-Sugeno fuzzy systems. GEFS Shinji Fukuda, Jun Nakajima, Bernard De Baets, Willem Waegeman, Takahiko Mukai, Ans M. Mouton, Norio Onikura
2011	J	`jnl`	A kernel-based framework for learning graded relations from data CoRR Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets
2011	—	`conf`	An Exact Algorithm for F-Measure Maximization. NIPS Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
2011	J	`jnl`	An experimental comparison of cross-validation techniques for estimating the area under the ROC curve. Comput. Stat. Data Anal. Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski
2011	C	`conf`	ERA ranking representability: The missing link between ordinal regression and multi-class classification. ISDA Willem Waegeman, Bernard De Baets
2011	—	`conf`	Learning Valued Relations from Data. Eurofuse Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets
2011	—	`conf`	Modelling Fish Habitat Preference with a Genetic Algorithm-Optimized Takagi-Sugeno Model Based on Pairwise Comparisons. Eurofuse Shinji Fukuda, Willem Waegeman, Ans M. Mouton, Bernard De Baets
2011	J	`jnl`	On the ERA ranking representability of pairwise bipartite ranking functions. Artif. Intell. Willem Waegeman, Bernard De Baets
2011	J	`jnl`	Supervised learning algorithms for multi-class classification problems with partial class memberships. Fuzzy Sets Syst. Willem Waegeman, Jan Verwaeren, Bram Slabbinck, Bernard De Baets
2010	—	`ch.`	A Survey on ROC-based Ordinal Regression. Preference Learning Willem Waegeman, Bernard De Baets
2010	—	`conf`	A comparison of AUC estimators in small-sample studies. MLSB Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski
2010	—	`conf`	A genetic Takagi-Sugeno fuzzy system for fish habitat preference modelling. NaBIC Shinji Fukuda, Norio Onikura, Bernard De Baets, Willem Waegeman, Ans M. Mouton, Jun Nakajima, Takahiko Mukai
2010	J	`jnl`	A transitivity analysis of bipartite rankings in pairwise multi-class classification. Inf. Sci. Willem Waegeman, Bernard De Baets
2010	—	`conf`	Conditional Ranking on Relational Data. ECML/PKDD (2) Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski, Bernard De Baets
2010	B	`conf`	Directional predictions for 4-class BCI data. ESANN Dieter Devlaminck, Willem Waegeman, Bruno Bauwens, Bart Wyns, Georges Otte, Luc Boullart, Patrick Santens
2010	J	`jnl`	From learning taxonomies to phylogenetic learning: Integration of 16S rRNA gene data into FAME-based bacterial classification. BMC Bioinform. Bram Slabbinck, Willem Waegeman, Peter Dawyndt, Paul De Vos, Bernard De Baets
2010	J	`jnl`	Learning intransitive reciprocal relations with kernel methods. Eur. J. Oper. Res. Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, Tapio Salakoski, Bernard De Baets
2010	—	`conf`	Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss. ECML/PKDD (1) Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
2009	J	`jnl`	Kernel-based learning methods for preference aggregation. 4OR Willem Waegeman, Bernard De Baets, Luc Boullart
2009	J	`jnl`	Learning to rank: a ROC-based graph-theoretic approach. 4OR Willem Waegeman
2008	J	`jnl`	Classifying carpets based on laser scanner data. Eng. Appl. Artif. Intell. Willem Waegeman, Johannes Cottyn, Bart Wyns, Luc Boullart, Bernard De Baets, Lieva Van Langenhove, Jan Detand
2008	J	`jnl`	Learning layered ranking functions with structured support vector machines. Neural Networks Willem Waegeman, Bernard De Baets, Luc Boullart
2008	J	`jnl`	On the scalability of ordered multi-class ROC analysis. Comput. Stat. Data Anal. Willem Waegeman, Bernard De Baets, Luc Boullart
2008	J	`jnl`	ROC analysis in ordinal regression learning. Pattern Recognit. Lett. Willem Waegeman, Bernard De Baets, Luc Boullart