 (6) Machine-learning-based prediction of disability progression in multiple sclerosis: an observational, international, multi-center studyE. De Brouwer, T. Becker, L. Werthen-Brabants, P. Dewulf, D. Iliadis, C. Dekeyser, G. Lure's, B. Van Wijmeersch, V. Popescu, T. Dhaene, D. Deschrijver, W. Waegeman, B. De Baets, M. Stock, D. Horakova, E.K. Havrdova, S. Ozakbas, F. Pattio, G. Izquierdo, S. Eichau, A. Prat, M. Girard, M. Onofrj, A. Lugaresi, P. Grammond, T. Kalincik, R. Alroughani, Y. Moreau and L. Peeters(2024) PLOS DIGITAL HEALTH. 3, e0000533. |
| (5) Pitfalls of epistemic uncertainty quantification through loss minimisationV. Bengs, E. Hüllermeier and W. Waegeman(2022) 36TH CONFERENCE ON NEURAL INFORMATION PROCESSING SYSTEMS (NEURIPS 2022), PROCEEDINGS. , . |
| (4) On Second-Order Scoring Rules for Epistemic Uncertainty QuantificationV. Bengs, E. Hüllermeier, W. Waegeman(2023) INTERNATIONAL CONFERENCE ON MACHINE LEARNING. 202, 2078-2091. |
| (3) On the Calibration of Probabilistic Classifier SetsT. Mortier, V. Bengs, E. Hüllermeier, S. Luca and W. Waegeman(2023) INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS. 26, 8857-8870. |
| (2) Seasonal prediction of Horn of Africa long rains using machine learning: The pitfalls of preselecting correlated predictorsV.M.H. Deman, A. Koppa, W. Waegeman, D.A. Macleod, M. Bliss Singer and D.G. Miralles(2022) FRONTIERS IN WATER. 4, 1053020. |
| (1) Lyapunov profiles of three-state totalistic cellular automataM. Vispoel, A.J. Daly and J.M. Baetens(2022) LECTURE NOTES IN COMPUTER SCIENCE. 13402, 106-115. |