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2020 – today
- 2024
[j36]Dimitrios Iliadis
, Marcel Wever, Bernard De Baets, Willem Waegeman:
Hyperparameter optimization of two-branch neural networks in multi-target prediction. Appl. Soft Comput. 165: 111957 (2024)- [j35]Dimitrios Iliadis, Bernard De Baets, Tapio Pahikkala, Willem Waegeman:
A comparison of embedding aggregation strategies in drug-target interaction prediction. BMC Bioinform. 25(1): 59 (2024) - [c28]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? ICML 2024 - [i22]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR abs/2402.09056 (2024) - [i21]Nina Schmid, David Fernandes del Pozo, Willem Waegeman, Jan Hasenauer:
Assessment of Uncertainty Quantification in Universal Differential Equations. CoRR abs/2406.08853 (2024) - 2023
- [j34]Nicolas Dewolf, Bernard De Baets, Willem Waegeman:
Valid prediction intervals for regression problems. Artif. Intell. Rev. 56(1): 577-613 (2023) - [j33]Dimitrios Iliadis, Bernard De Baets, Willem Waegeman:
DeepMTP: A Python-based deep learning framework for multi-target prediction. SoftwareX 23: 101516 (2023) - [c27]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On the Calibration of Probabilistic Classifier Sets. AISTATS 2023: 8857-8870 - [c26]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML 2023: 2078-2091 - [i20]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR abs/2301.12736 (2023) - [i19]Nicolas Dewolf, Bernard De Baets, Willem Waegeman:
Heteroskedastic conformal regression. CoRR abs/2309.08313 (2023) - 2022
- [j32]Gaetan De Waele, Jim Clauwaert, Gerben Menschaert, Willem Waegeman:
CpG Transformer for imputation of single-cell methylomes. Bioinform. 38(3): 597-603 (2022) - [j31]Dimitrios Iliadis, Bernard De Baets, Willem Waegeman:
Multi-target prediction for dummies using two-branch neural networks. Mach. Learn. 111(2): 651-684 (2022) - [j30]Jim Clauwaert, Willem Waegeman:
Novel Transformer Networks for Improved Sequence Labeling in genomics. IEEE ACM Trans. Comput. Biol. Bioinform. 19(1): 97-106 (2022) - [c25]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS 2022 - [c24]Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. UAI 2022: 1392-1401 - [i18]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 abs/2203.06102 (2022) - [i17]Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. CoRR abs/2203.06676 (2022) - [i16]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On Calibration of Ensemble-Based Credal Predictors. CoRR abs/2205.10082 (2022) - [i15]Dimitrios Iliadis, Marcel Wever, Bernard De Baets, Willem Waegeman:
Hyperparameter optimization in deep multi-target prediction. CoRR abs/2211.04362 (2022) - 2021
- [j29]Jim Clauwaert, Gerben Menschaert, Willem Waegeman:
Explainability in transformer models for functional genomics. Briefings Bioinform. 22(5) (2021) - [j28]Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman:
Efficient set-valued prediction in multi-class classification. Data Min. Knowl. Discov. 35(4): 1435-1469 (2021) - [j27]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3): 457-506 (2021) - [i14]Dimitrios Iliadis, Bernard De Baets, Willem Waegeman:
Automated problem setting selection in multi-target prediction with AutoMTP. CoRR abs/2104.09967 (2021) - [i13]Nicolas Dewolf, Bernard De Baets, Willem Waegeman:
Well-calibrated prediction intervals for regression problems. CoRR abs/2107.00363 (2021) - 2020
- [j26]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): 262-271 (2020)
2010 – 2019
- 2019
- [j25]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: 177-183 (2019) - [j24]Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier:
Multi-target prediction: a unifying view on problems and methods. Data Min. Knowl. Discov. 33(2): 293-324 (2019) - [c23]Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman:
Set-Valued Prediction in Multi-Class Classification. BNAIC/BENELEARN 2019 - [c22]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 - [c21]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: 416-431 - [i12]Thomas Mortier, Marek Wydmuch, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. CoRR abs/1906.08129 (2019) - [i11]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. CoRR abs/1910.09457 (2019) - 2018
- [j23]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) - [c20]Stijn Decubber, Thomas Mortier, Krzysztof Dembczynski, Willem Waegeman:
Deep F-Measure Maximization in Multi-label Classification: A Comparative Study. ECML/PKDD (1) 2018: 290-305 - [i10]Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
A Comparative Study of Pairwise Learning Methods based on Kernel Ridge Regression. CoRR abs/1803.01575 (2018) - [i9]Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier:
Multi-Target Prediction: A Unifying View on Problems and Methods. CoRR abs/1809.02352 (2018) - 2017
- [c19]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: 1-3 - [c18]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: 1-3 - [c17]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: 15-26 - 2016
- [j22]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): 1370-1394 (2016) - [c16]Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier:
Consistency of Probabilistic Classifier Trees. ECML/PKDD (2) 2016: 511-526 - [i8]Michiel Stock, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method. CoRR abs/1606.04275 (2016) - [i7]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 abs/1606.04278 (2016) - 2015
- [j21]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: 179-185 (2015) - [i6]Tapio Pahikkala, Markus Viljanen, Antti Airola, Willem Waegeman:
Spectral Analysis of Symmetric and Anti-Symmetric Pairwise Kernels. CoRR abs/1506.05950 (2015) - 2014
- [j20]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: 69-80 (2014) - [j19]Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier:
On the bayes-optimality of F-measure maximizers. J. Mach. Learn. Res. 15(1): 3333-3388 (2014) - [j18]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): 1157-1169 (2014) - [c15]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: 517-532 - [i5]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 abs/1405.4394 (2014) - [i4]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 abs/1405.4423 (2014) - 2013
- [j17]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: 1-6 (2013) - [j16]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): 321-356 (2013) - [c14]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: 1130-1138 - [i3]Willem Waegeman, Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier:
On the Bayes-optimality of F-measure maximizers. CoRR abs/1310.4849 (2013) - 2012
- [j15]Jan Verwaeren, Willem Waegeman, Bernard De Baets:
Learning partial ordinal class memberships with kernel-based proportional odds models. Comput. Stat. Data Anal. 56(4): 928-942 (2012) - [j14]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
On label dependence and loss minimization in multi-label classification. Mach. Learn. 88(1-2): 5-45 (2012) - [j13]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): 1090-1101 (2012) - [c13]Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier:
An Analysis of Chaining in Multi-Label Classification. ECAI 2012: 294-299 - [c12]Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker:
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. NIPS 2012: 2510-2518 - [c11]Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier, Adrian Jaroszewicz, Willem Waegeman:
F-Measure Maximization in Topical Classification. RSCTC 2012: 439-446 - [i2]Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman:
Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data. CoRR abs/1209.4825 (2012) - 2011
- [j12]Willem Waegeman, Bernard De Baets:
On the ERA ranking representability of pairwise bipartite ranking functions. Artif. Intell. 175(7-8): 1223-1250 (2011) - [j11]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): 1828-1844 (2011) - [j10]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): 106-125 (2011) - [c10]Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Bernard De Baets:
Learning Valued Relations from Data. Eurofuse 2011: 257-268 - [c9]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: 375-387 - [c8]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: 81-86 - [c7]Willem Waegeman, Bernard De Baets:
ERA ranking representability: The missing link between ordinal regression and multi-class classification. ISDA 2011: 1188-1193 - [c6]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
An Exact Algorithm for F-Measure Maximization. NIPS 2011: 1404-1412 - [i1]Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets:
A kernel-based framework for learning graded relations from data. CoRR abs/1111.6473 (2011) - 2010
- [j9]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: 69 (2010) - [j8]Tapio Pahikkala, Willem Waegeman, Evgeni Tsivtsivadze, Tapio Salakoski, Bernard De Baets:
Learning intransitive reciprocal relations with kernel methods. Eur. J. Oper. Res. 206(3): 676-685 (2010) - [j7]Willem Waegeman, Bernard De Baets:
A transitivity analysis of bipartite rankings in pairwise multi-class classification. Inf. Sci. 180(21): 4099-4117 (2010) - [c5]Dieter Devlaminck, Willem Waegeman, Bruno Bauwens, Bart Wyns, Georges Otte, Luc Boullart, Patrick Santens:
Directional predictions for 4-class BCI data. ESANN 2010 - [c4]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: 274-279 - [c3]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: 280-295 - [c2]Tapio Pahikkala, Willem Waegeman, Antti Airola, Tapio Salakoski, Bernard De Baets:
Conditional Ranking on Relational Data. ECML/PKDD (2) 2010: 499-514 - [c1]Antti Airola, Tapio Pahikkala, Willem Waegeman, Bernard De Baets, Tapio Salakoski:
A comparison of AUC estimators in small-sample studies. MLSB 2010: 3-13 - [p1]Willem Waegeman, Bernard De Baets:
A Survey on ROC-based Ordinal Regression. Preference Learning 2010: 127-154
2000 – 2009
- 2009
- [j6]Willem Waegeman, Bernard De Baets, Luc Boullart:
Kernel-based learning methods for preference aggregation. 4OR 7(2): 169-189 (2009) - [j5]Willem Waegeman:
Learning to rank: a ROC-based graph-theoretic approach. 4OR 7(4): 399-402 (2009) - 2008
- [j4]Willem Waegeman, Bernard De Baets, Luc Boullart:
On the scalability of ordered multi-class ROC analysis. Comput. Stat. Data Anal. 52(7): 3371-3388 (2008) - [j3]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): 907-918 (2008) - [j2]Willem Waegeman, Bernard De Baets, Luc Boullart:
Learning layered ranking functions with structured support vector machines. Neural Networks 21(10): 1511-1523 (2008) - [j1]Willem Waegeman, Bernard De Baets, Luc Boullart:
ROC analysis in ordinal regression learning. Pattern Recognit. Lett. 29(1): 1-9 (2008)
Coauthor Index
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