Ghent University Academic Bibliography

Your filters: cql: author="Hüllermeier, Eyke" or (type any "bookEditor issueEditor" and editor="Hüllermeier, Eyke")

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A calibration test for evaluating set-based epistemic uncertainty representations

Mira Kristin Jürgens (UGent) , Thomas Mortier (UGent) , Eyke Hüllermeier, Viktor Bengs and Willem Waegeman (UGent)

(2025) MACHINE LEARNING. 114(9).
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- Miscellaneous
- open access
Valid prediction intervals for weather forecasting with conformal prediction

Thomas Mortier (UGent) , Cas Decancq (UGent) , Yusuf Sale, Alireza Javanmardi, Willem Waegeman (UGent) , Eyke Hüllermeier and Diego Miralles (UGent)

(2025)
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- Conference Paper
- C1
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Is epistemic uncertainty faithfully represented by evidential deep learning methods?

Mira Kristin Jürgens (UGent) , Nis Meinert, Viktor Bengs, Eyke Hüllermeier and Willem Waegeman (UGent)

(2024) 41st International Conference on Machine Learning (ICML), Proceedings. p.22624-22642
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- P1
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On second-order scoring rules for epistemic uncertainty quantification

Viktor Bengs, Eyke Hüllermeier and Willem Waegeman (UGent)

(2023) INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 202. In Proceedings of Machine Learning Research 202. p.2078-2091
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On the calibration of probabilistic classifier sets

Thomas Mortier (UGent) , Viktor Bengs, Eyke Hüllermeier, Stijn Luca (UGent) and Willem Waegeman (UGent)

(2023) INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 206. In Proceedings of Machine Learning Research 206. p.8857-8870
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Pitfalls of epistemic uncertainty quantification through loss minimisation

Viktor Bengs, Eyke Hüllermeier and Willem Waegeman (UGent)

(2022) 36th Conference on Neural Information Processing Systems (NeurIPS 2022), Proceedings.
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Set-valued prediction in hierarchical classification with constrained representation complexity

Thomas Mortier (UGent) , Eyke Hüllermeier, Krzysztof Dembczynski and Willem Waegeman (UGent)

(2022) Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence. In Proceedings of Machine Learning Research 180. p.1392-1401
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Multi-target prediction : a unifying view on problems and methods

Willem Waegeman (UGent) , Krzysztof Dembczyński and Eyke Hüllermeier

(2019) DATA MINING AND KNOWLEDGE DISCOVERY. 33(2). p.293-324
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- Conference Paper
- C3
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Set-Valued Prediction in Multi-Class Classification

Thomas Mortier (UGent) , Marek Wydmuch, Krzysztof Dembczyński, Eyke Hüllermeier and Willem Waegeman (UGent)

(2019) BNAIC/BENELEARN 2019 : proceedings of the reference AI & ML conference for Belgium, Netherlands & Luxemburg. In CEUR Workshop Proceedings 2491.
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Consistency of probabilistic classifier trees

Krzysztof Dembczyński, Wojciech Kotłowski, Willem Waegeman (UGent) , Róbert Busa-Fekete and Eyke Hüllermeier

(2016) ECML PKDD 2016 : machine learning and knowledge discovery in databases. In Lecture Notes in Computer Science 9852. p.511-526

Academic Bibliography

A calibration test for evaluating set-based epistemic uncertainty representations

Valid prediction intervals for weather forecasting with conformal prediction

Is epistemic uncertainty faithfully represented by evidential deep learning methods?

On second-order scoring rules for epistemic uncertainty quantification

On the calibration of probabilistic classifier sets

Pitfalls of epistemic uncertainty quantification through loss minimisation

Set-valued prediction in hierarchical classification with constrained representation complexity

Multi-target prediction : a unifying view on problems and methods

Set-Valued Prediction in Multi-Class Classification

Consistency of probabilistic classifier trees

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