1 citations · 2 across the 5 of their papers we have counts for
7 papers
Weather Emulators at the Frontier of Heat Extremes Predictability
Cas Decancq, Thomas Mortier, Jessica Keune +1
Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states. Yet…
Chasing the Timber Trail: Machine Learning to Reveal Harvest Location Misrepresentation
Shailik Sarkar, Raquib Bin Yousuf, Linhan Wang +9
Illegal logging poses a significant threat to global biodiversity, climate stability, and depresses international prices for legal wood harvesting and responsible forest products t…
A calibration test for evaluating set-based epistemic uncertainty representations
Mira Jürgens, Thomas Mortier, Eyke Hüllermeier +2
The accurate representation of epistemic uncertainty is a challenging yet essential task in machine learning. A widely used representation corresponds to convex sets of probabilist…
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Thomas Mortier, Alireza Javanmardi, Yusuf Sale +2
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conform…
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…
Efficient Set-Valued Prediction in Multi-Class Classification
Thomas Mortier, Marek Wydmuch, Krzysztof Dembczyński +2
In cases of uncertainty, a multi-class classifier preferably returns a set of candidate classes instead of predicting a single class label with little guarantee. More precisely, th…