53 citations · 69 across the 6 of their papers we have counts for
5 papers · 1 filter
Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
Kacper Sokol, Santo M. A. R. Thies, Eyke Hüllermeier
In this paper we argue that, to its detriment, transparency research overlooks many foundational concepts of artificial intelligence. As an illustrating example we focus on uncerta…
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier +1
Explainable Artificial Intelligence (XAI) has mainly focused on static learning scenarios so far. We are interested in dynamic scenarios where data is sampled progressively, and le…
Memorization-Dilation: Modeling Neural Collapse Under Label Noise
Duc Anh Nguyen, Ron Levie, Julian Lienen +2
The notion of neural collapse refers to several emergent phenomena that have been empirically observed across various canonical classification problems. During the terminal phase o…
Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities
Eyke Hüllermeier
Recent applications of machine learning (ML) reveal a noticeable shift from its use for predictive modeling in the sense of a data-driven construction of models mainly used for the…
Identification of functionally related enzymes by learning-to-rank methods
Michiel Stock, Thomas Fober, Eyke Hüllermeier +6
Enzyme sequences and structures are routinely used in the biological sciences as queries to search for functionally related enzymes in online databases. To this end, one usually de…