Willem Waegeman

1. Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
   2022/03/11 by Viktor Bengs, Eyke Hüllermeier, Bengs, Viktor +3 · 5 citations
   Computer Science · #Machine Learning and Algorithms #Machine Learning and Data Classification #Explainable Artificial Intelligence (XAI)
2. On Second-Order Scoring Rules for Epistemic Uncertainty Quantification
   2023/01/30 by Viktor Bengs, Bengs, Viktor, Eyke Hüllermeier +3 · 3 citations
   Computer Science · #68T37 (Primary) 68T30 (Secondary) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Machine Learning and Data Classification
3. A two-step learning approach for solving full and almost full cold start problems in dyadic prediction
   2014/05/17 by Tapio Pahikkala, Michiel Stock, Pahikkala, Tapio +9 · 1 citation
   Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Machine Learning and Data Classification #Tensor decomposition and applications
4. Assessment of Uncertainty Quantification in Universal Differential Equations
   2024/06/13 by Nina Schmid, Schmid, Nina, David Fernandes del Pozo +5 · 3 citations
   Decision Sciences · #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design #Quantitative Methods (q-bio.QM)
5. Conditional validity of heteroskedastic conformal regression
   2023/09/15 by Nicolas Dewolf, Bernard De Baets, Dewolf, Nicolas +3 · 1 citation
   Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference
6. A calibration test for evaluating set-based epistemic uncertainty representations
   2025/02/22 by Mira Jürgens, Thomas Mortier, Jürgens, Mira +7 · 1 citation
   Decision Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probabilistic and Robust Engineering Design
7. Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
   2026/07/16 by Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier +1
   #cs.LG #cs.AI