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Conformal Prediction Intervals for Remaining Useful Lifetime Estimation
Alireza Javanmardi, Eyke Hüllermeier
The main objective of Prognostics and Health Management is to estimate the Remaining Useful Lifetime (RUL), namely, the time that a system or a piece of equipment is still in worki…
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…
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures?
Lisa Wimmer, Yusuf Sale, Paul Hofman +2
The quantification of aleatoric and epistemic uncertainty in terms of conditional entropy and mutual information, respectively, has recently become quite common in machine learning…
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark
Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus +1
In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy…
On the Calibration of Probabilistic Classifier Sets
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier +2
Multi-class classification methods that produce sets of probabilistic classifiers, such as ensemble learning methods, are able to model aleatoric and epistemic uncertainty. Aleator…
Set-valued prediction in hierarchical classification with constrained representation complexity
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński +1
Set-valued prediction is a well-known concept in multi-class classification. When a classifier is uncertain about the class label for a test instance, it can predict a set of class…
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Uncertainty quantification has received increasing attention in machine learning in the recent past. In particular, a distinction between aleatoric and epistemic uncertainty has be…
Non-Stationary Dueling Bandits
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
We study the non-stationary dueling bandits problem with arms, where the time horizon consists of stationary segments, each of which is associated with its own preferen…
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
We consider the regret minimization task in a dueling bandits problem with context information. In every round of the sequential decision problem, the learner makes a context-depen…
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget
Jasmin Brandt, Viktor Bengs, Björn Haddenhorst +1
We consider the combinatorial bandits problem with semi-bandit feedback under finite sampling budget constraints, in which the learner can carry out its action only for a limited n…