| Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? L Wimmer, Y Sale, P Hofman, B Bischl, E Hüllermeier Uncertainty in Artificial Intelligence, 2282-2292, 2023 | 32 | 2023 |
| Second-order uncertainty quantification: Variance-based measures Y Sale, P Hofman, L Wimmer, E Hüllermeier, T Nagler arXiv preprint arXiv:2401.00276, 2023 | 4 | 2023 |
| Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules P Hofman, Y Sale, E Hüllermeier arXiv preprint arXiv:2404.12215, 2024 | 3 | 2024 |
| Using conceptors to overcome catastrophic forgetting in convolutional neural networks P Hofman | 2 | 2021 |
| Conformal Prediction with Partially Labeled Data A Javanmardi, Y Sale, P Hofman, E Hüllermeier Conformal and Probabilistic Prediction with Applications, 251-266, 2023 | 1 | 2023 |
| Label-wise Aleatoric and Epistemic Uncertainty Quantification Y Sale, P Hofman, T Löhr, L Wimmer, T Nagler, E Hüllermeier arXiv preprint arXiv:2406.02354, 2024 | | 2024 |
| Quantifying Aleatoric and Epistemic Uncertainty: A Credal Approach P Hofman, Y Sale, E Hüllermeier ICML 2024 Workshop on Structured Probabilistic Inference {\&} Generative …, 0 | | |
| Identifying Trends in Feature Attributions during Training of Neural Networks E Terzieva, M Muschalik, P Hofman, E Hüllermeier | | |
| Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning L Wimmer, Y Sale, P Hofman, B Bischl, E Hüllermeier | | |
