Eyke Hüllermeier

ORCID

Publication Activity (10 Years)

Years Active: 1997-2024
Publications (10 Years): 283

Top Venues

University of Paderborn, Germany (PHD)

Education History

University of Paderborn, Germany

PHD

Publications

Timo Kaufmann, Jannis Blüml, Antonia Wüst, Quentin Delfosse, Kristian Kersting, Eyke Hüllermeier
OCALM: Object-Centric Assessment with Language Models. CoRR (2024)
Yusuf Sale, Paul Hofman, Lisa Wimmer, Eyke Hüllermeier, Thomas Nagler
Second-Order Uncertainty Quantification: Variance-Based Measures. CoRR (2024)
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
Identifying Copeland Winners in Dueling Bandits with Indifferences. AISTATS (2024)
Stefan Heid, Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier
Learning decision catalogues for situated decision making: The case of scoring systems. Int. J. Approx. Reason. 171 (2024)
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions. CoRR (2024)
Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. CoRR (2024)
Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier
Label-wise Aleatoric and Epistemic Uncertainty Quantification. CoRR (2024)
Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven
Continual Learning: Applications and the Road Forward. Trans. Mach. Learn. Res. 2024 (2024)
Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. CoRR (2024)
Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier
Probabilistic Scoring Lists for Interpretable Machine Learning. CoRR (2024)
Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization. ICLR (2024)
Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee
A Novel Bayes' Theorem for Upper Probabilities. Epi UAI (2024)
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. AAAI (2024)
Pritha Gupta, Marcel Wever, Eyke Hüllermeier
Information Leakage Detection through Approximate Bayes-optimal Prediction. CoRR (2024)
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR (2024)
Arduin Findeis, Timo Kaufmann, Eyke Hüllermeier, Samuel Albanie, Robert Mullins
Inverse Constitutional AI: Compressing Preferences into Principles. CoRR (2024)
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. AISTATS (2024)
Julian Lienen, Eyke Hüllermeier
Mitigating Label Noise through Data Ambiguation. AAAI (2024)
Eyke Hüllermeier, Roman Slowinski
Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies-part I. 4OR 22 (2) (2024)
Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. CoRR (2024)
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. CoRR (2024)
Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR (2024)
Timo Löhr, Michael Ingrisch, Eyke Hüllermeier
Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. AIME (2) (2024)
Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier
Approximating the Shapley Value without Marginal Contributions. AAAI (2024)
Clemens Damke, Eyke Hüllermeier
Linear Opinion Pooling for Uncertainty Quantification on Graphs. CoRR (2024)
Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
Pairwise Difference Learning for Classification. CoRR (2024)
Alireza Javanmardi, David Stutz, Eyke Hüllermeier
Conformalized Credal Set Predictors. CoRR (2024)
Paul Hofman, Yusuf Sale, Eyke Hüllermeier
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules. CoRR (2024)
Eli Verwimp, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven
Continual Learning: Applications and the Road Forward. CoRR (2023)
Pritha Gupta, Jan Peter Drees, Eyke Hüllermeier
Automated Side-Channel Attacks using Black-Box Neural Architecture Search. IACR Cryptol. ePrint Arch. 2023 (2023)
Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee
A Novel Bayes' Theorem for Upper Probabilities. CoRR (2023)
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
Approximating the Shapley Value without Marginal Contributions. CoRR (2023)
Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier
Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI (2023)
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
Identifying Copeland Winners in Dueling Bandits with Indifferences. CoRR (2023)
Svenja Uhlemeyer, Julian Lienen, Eyke Hüllermeier, Hanno Gottschalk
Detecting Novelties with Empty Classes. CoRR (2023)
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
On the Calibration of Probabilistic Classifier Sets. AISTATS (2023)
Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. xAI (1) (2023)
Mohamed Karim Belaid, Dorra El Mekki, Maximilian Rabus, Eyke Hüllermeier
Optimizing Data Shapley Interaction Calculation from O(2^n) to O(t n^2) for KNN models. CoRR (2023)
Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier
Probabilistic Scoring Lists for Interpretable Machine Learning. DS (2023)
Yusuf Sale, Michele Caprio, Eyke Hüllermeier
Is the volume of a credal set a good measure for epistemic uncertainty? UAI (2023)
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer
On Feature Removal for Explainability in Dynamic Environments. ESANN (2023)
Pritha Gupta, Jan Peter Drees, Eyke Hüllermeier
Automated Side-Channel Attacks using Black-Box Neural Architecture Search. ARES (2023)
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. CoRR (2023)
Julian Lienen, Eyke Hüllermeier
Mitigating Label Noise through Data Ambiguation. CoRR (2023)
Stefan Haas, Eyke Hüllermeier
Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing. ECML/PKDD (6) (2023)
Arnab Sharma, Vitalik Melnikov, Eyke Hüllermeier, Heike Wehrheim
Property-Driven Black-Box Testing of Numeric Functions. Software Engineering (2023)
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. CoRR (2023)
Tanja Tornede, Alexander Tornede, Jonas Hanselle, Felix Mohr, Marcel Wever, Eyke Hüllermeier
Towards Green Automated Machine Learning: Status Quo and Future Directions. J. Artif. Intell. Res. 77 (2023)
Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS (2023)
Viktor Bengs, Eyke Hüllermeier
Multi-armed bandits with censored consumption of resources. Mach. Learn. 112 (1) (2023)
Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). IJCAI (2023)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR (2023)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML (2023)
Julian Lienen, Caglar Demir, Eyke Hüllermeier
Conformal Credal Self-Supervised Learning. COPA (2023)
Jasmin Brandt, Elias Schede, Shivam Sharma, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. LWDA (2023)
Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier
A Survey of Reinforcement Learning from Human Feedback. CoRR (2023)
Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier
Algorithm selection on a meta level. Mach. Learn. 112 (4) (2023)
Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI (2023)
Duc Anh Nguyen, Ron Levie, Julian Lienen, Eyke Hüllermeier, Gitta Kutyniok
Memorization-Dilation: Modeling Neural Collapse Under Noise. ICLR (2023)
Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche
Koopman Kernel Regression. NeurIPS (2023)
Mohamed Karim Belaid, Richard Bornemann, Maximilian Rabus, Ralf Krestel, Eyke Hüllermeier
Compare-xAI: Toward Unifying Functional Testing Methods for Post-hoc XAI Algorithms into a Multi-dimensional Benchmark. xAI (2) (2023)
Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche
Koopman Kernel Regression. CoRR (2023)
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. ECML/PKDD (3) (2023)
Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier
Iterative Deepening Hyperband. CoRR (2023)
Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
Probabilistic Self-supervised Learning via Scoring Rules Minimization. CoRR (2023)
Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. CoRR (2023)
Jonas Hanselle, Jaroslaw Kornowicz, Stefan Heid, Kirsten Thommes, Eyke Hüllermeier
Comparing Humans and Algorithms in Feature Ranking: A Case-Study in the Medical Domain. LWDA (2023)
Marcel Wever, Miran Özdogan, Eyke Hüllermeier
Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. GECCO (2023)
Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, Karlson Pfannschmidt
Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. SIAM J. Sci. Comput. 45 (2) (2023)
Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
Second-Order Uncertainty Quantification: A Distance-Based Approach. CoRR (2023)
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer
Incremental permutation feature importance (iPFI): towards online explanations on data streams. Mach. Learn. 112 (12) (2023)
Anna-Katharina Wickert, Clemens Damke, Lars Baumgärtner, Eyke Hüllermeier, Mira Mezini
UnGoML: Automated Classification of unsafe Usages in Go. MSR (2023)
Amirhossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning. CoRR (2023)
Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier
Conformal Prediction with Partially Labeled Data. CoRR (2023)
Anna-Katharina Wickert, Clemens Damke, Lars Baumgärtner, Eyke Hüllermeier, Mira Mezini
UNGOML: Automated Classification of unsafe Usages in Go. CoRR (2023)
Yusuf Sale, Michele Caprio, Eyke Hüllermeier
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? CoRR (2023)
Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier
Conformal Prediction with Partially Labeled Data. COPA (2023)
Sascha Henzgen, Eyke Hüllermeier
Weighting by Tying: A New Approach to Weighted Rank Correlation. CoRR (2023)
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
Agnostic Explanation of Model Change based on Feature Importance. Künstliche Intell. 36 (3) (2022)
Arunselvan Ramaswamy, Eyke Hüllermeier
Deep Q-Learning: Theoretical Insights From an Asymptotic Analysis. IEEE Trans. Artif. Intell. 3 (2) (2022)
Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus, Ralf Krestel
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark. CoRR (2022)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. CoRR (2022)
Duc Anh Nguyen, Ron Levie, Julian Lienen, Gitta Kutyniok, Eyke Hüllermeier
Memorization-Dilation: Modeling Neural Collapse Under Noise. CoRR (2022)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS (2022)
Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams. CoRR (2022)
Julian Rodemann, Dominik Kreiss, Eyke Hüllermeier, Thomas Augustin
Levelwise Data Disambiguation by Cautious Superset Classification. SUM (2022)
Eyke Hüllermeier
Representation and quantification of uncertainty in machine learning. LFA (2022)
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. CoRR (2022)
Eyke Hüllermeier, Sébastien Destercke, Mohammad Hossein Shaker
Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison. UAI (2022)
Eyke Hüllermeier
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? CoRR (2022)
Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp
A flexible class of dependence-aware multi-label loss functions. Mach. Learn. 111 (2) (2022)
Vu-Linh Nguyen, Mohammad Hossein Shaker, Eyke Hüllermeier
How to measure uncertainty in uncertainty sampling for active learning. Mach. Learn. 111 (1) (2022)
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
On Calibration of Ensemble-Based Credal Predictors. CoRR (2022)
Stefan Haas, Eyke Hüllermeier
A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain. ECML/PKDD (6) (2022)
Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman
Set-valued prediction in hierarchical classification with constrained representation complexity. UAI (2022)
Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier
Learning context-dependent choice functions. Int. J. Approx. Reason. 140 (2022)
Arnab Sharma, Vitalik Melnikov, Eyke Hüllermeier, Heike Wehrheim
Property-Driven Testing of Black-Box Functions. FormaliSE@ICSE (2022)
Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. NeurIPS (2022)
Stefanie Schneider, Matthias Springstein, Javad Rahnama, Hubertus Kohle, Ralph Ewerth, Eyke Hüllermeier
iART - Eine Suchmaschine zur Unterstützung von bildorientierten Forschungsprozessen. DHd (2022)
Andrea Campagner, Julian Lienen, Eyke Hüllermeier, Davide Ciucci
Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. IJCRS (2022)

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Publication Activity (10 Years)

Top Topics

Top Venues

Eyke Hüllermeier