Hüllermeier, Eyke

144 publications

MLJ 2026 Uncertainty Quantification in Pairwise Difference Learning for Classification Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier
MLJ 2025 A Calibration Test for Evaluating Set-Based Epistemic Uncertainty Representations Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman
TMLR 2025 A Survey of Reinforcement Learning from Human Feedback Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier
NeurIPS 2025 Adjusted Count Quantification Learning on Graphs Clemens Damke, Eyke Hüllermeier
ICML 2025 Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries Xuening Feng, Zhaohui Jiang, Timo Kaufmann, Eyke Hüllermeier, Paul Weng, Yifei Zhu
ISIPTA 2025 Conformal Prediction Regions Are Imprecise Highest Density Regions Michele Caprio, Yusuf Sale, Eyke Hüllermeier
UAI 2025 Conformal Prediction Without Nonconformity Scores Jonas Hanselle, Alireza Javanmardi, Tobias Florin Oberkofler, Yusuf Sale, Eyke Hüllermeier
NeurIPS 2025 Credal Prediction Based on Relative Likelihood Timo Löhr, Paul Hofman, Felix Mohr, Eyke Hüllermeier
AAAI 2025 DUO: Diverse, Uncertain, On-Policy Query Generation and Selection for Reinforcement Learning from Human Feedback Xuening Feng, Zhaohui Jiang, Timo Kaufmann, Puchen Xu, Eyke Hüllermeier, Paul Weng, Yifei Zhu
ECML-PKDD 2025 Distribution Matching for Graph Quantification Under Structural Covariate Shift Clemens Damke, Eyke Hüllermeier
ICLR 2025 Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto, Janine Strotherm, Luca Hermes, Alessandro Sperduti, Eyke Hüllermeier, Barbara Hammer
ECML-PKDD 2025 Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration for Exosuit Personalization Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio
NeurIPS 2025 Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions Hubert Baniecki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Przemyslaw Biecek
ICLR 2025 Inverse Constitutional AI: Compressing Preferences into Principles Arduin Findeis, Timo Kaufmann, Eyke Hüllermeier, Samuel Albanie, Robert D. Mullins
MLJ 2025 Probabilistic Scoring Lists for Interpretable Machine Learning Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier
NeurIPS 2025 ResponseRank: Data-Efficient Reward Modeling Through Preference Strength Learning Timo Kaufmann, Yannick Metz, Daniel A. Keim, Eyke Hüllermeier
AISTATS 2025 Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer, Julia Herbinger
ICML 2025 X-Hacking: The Threat of Misguided AutoML Rahul Sharma, Sumantrak Mukherjee, Andrea Sipka, Eyke Hüllermeier, Sebastian Josef Vollmer, Sergey Redyuk, David Antony Selby
AAAI 2024 Approximating the Shapley Value Without Marginal Contributions Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier
IJCAI 2024 Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO Jasmin Brandt, Marcel Wever, Viktor Bengs, Eyke Hüllermeier
AAAI 2024 Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
ECML-PKDD 2024 CUQ-GNN: Committee-Based Graph Uncertainty Quantification Using Posterior Networks Clemens Damke, Eyke Hüllermeier
ICMLW 2024 Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries Xuening Feng, Zhaohui Jiang, Timo Kaufmann, Eyke Hüllermeier, Paul Weng, Yifei Zhu
NeurIPS 2024 Conformalized Credal Set Predictors Alireza Javanmardi, David Stutz, Eyke Hüllermeier
ICMLW 2024 Conformalized Credal Set Predictors Alireza Javanmardi, David Stutz, Eyke Hüllermeier
TMLR 2024 Continual Learning: Applications and the Road Forward 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
ECML-PKDD 2024 Diversified Ensemble of Independent Sub-Networks for Robust Self-Supervised Representation Learning Amihossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
AISTATS 2024 Identifying Copeland Winners in Dueling Bandits with Indifferences Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
ICML 2024 Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? Mira Juergens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
ICML 2024 KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
UAI 2024 Label-Wise Aleatoric and Epistemic Uncertainty Quantification Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier
UAI 2024 Linear Opinion Pooling for Uncertainty Quantification on Graphs Clemens Damke, Eyke Hüllermeier
AAAI 2024 Mitigating Label Noise Through Data Ambiguation Julian Lienen, Eyke Hüllermeier
TMLR 2024 Piecewise-Stationary Dueling Bandits Patrick Kolpaczki, Eyke Hüllermeier, Viktor Bengs
ICML 2024 Position: Why We Must Rethink Empirical Research in Machine Learning Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
ICLR 2024 Probabilistic Self-Supervised Representation Learning via Scoring Rules Minimization Amirhossein Vahidi, Simon Schosser, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei
ICMLW 2024 Quantifying Aleatoric and Epistemic Uncertainty: A Credal Approach Paul Hofman, Yusuf Sale, Eyke Hüllermeier
ICMLW 2024 Relatively Rational: Learning Utilities and Rationalities Jointly from Pairwise Preferences Taku Yamagata, Tobias Oberkofler, Timo Kaufmann, Viktor Bengs, Eyke Hüllermeier, Raul Santos-Rodriguez
AISTATS 2024 SVARM-IQ: Efficient Approximation of Any-Order Shapley Interactions Through Stratification Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
ICML 2024 Second-Order Uncertainty Quantification: A Distance-Based Approach Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
NeurIPS 2024 Shapiq: Shapley Interactions for Machine Learning Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, Eyke Hüllermeier
IJCAI 2023 A Survey of Methods for Automated Algorithm Configuration (Extended Abstract) Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
AAAI 2023 AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
MLJ 2023 Algorithm Selection on a Meta Level Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier
MLJ 2023 Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer
UAI 2023 Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? Yusuf Sale, Michele Caprio, Eyke Hüllermeier
NeurIPS 2023 Koopman Kernel Regression Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche
ICLR 2023 Memorization-Dilation: Modeling Neural Collapse Under Noise Duc Anh Nguyen, Ron Levie, Julian Lienen, Eyke Hüllermeier, Gitta Kutyniok
MLJ 2023 Multi-Armed Bandits with Censored Consumption of Resources Viktor Bengs, Eyke Hüllermeier
ICML 2023 On Second-Order Scoring Rules for Epistemic Uncertainty Quantification Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
AISTATS 2023 On the Calibration of Probabilistic Classifier Sets Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
UAI 2023 Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? Lisa Wimmer, Yusuf Sale, Paul Hofman, Bernd Bischl, Eyke Hüllermeier
ECML-PKDD 2023 Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing Stefan Haas, Eyke Hüllermeier
NeurIPS 2023 SHAP-IQ: Unified Approximation of Any-Order Shapley Interactions Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer
JAIR 2023 Towards Green Automated Machine Learning: Status Quo and Future Directions Tanja Tornede, Alexander Tornede, Jonas Hanselle, Felix Mohr, Marcel Wever, Eyke Hüllermeier
ECML-PKDD 2023 iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier
MLJ 2022 A Flexible Class of Dependence-Aware Multi-Label Loss Functions Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp
ECML-PKDD 2022 A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain Stefan Haas, Eyke Hüllermeier
JAIR 2022 A Survey of Methods for Automated Algorithm Configuration Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
NeurIPS 2022 Finding Optimal Arms in Non-Stochastic Combinatorial Bandits with Semi-Bandit Feedback and Finite Budget Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
MLJ 2022 How to Measure Uncertainty in Uncertainty Sampling for Active Learning Vu-Linh Nguyen, Mohammad Hossein Shaker, Eyke Hüllermeier
AAAI 2022 Machine Learning for Online Algorithm Selection Under Censored Feedback Alexander Tornede, Viktor Bengs, Eyke Hüllermeier
NeurIPS 2022 Pitfalls of Epistemic Uncertainty Quantification Through Loss Minimisation Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
UAI 2022 Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison Eyke Hüllermeier, Sébastien Destercke, Mohammad Hossein Shaker
UAI 2022 Set-Valued Prediction in Hierarchical Classification with Constrained Representation Complexity Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczyński, Willem Waegeman
ICML 2022 Stochastic Contextual Dueling Bandits Under Linear Stochastic Transitivity Models Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
MLJ 2021 Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods Eyke Hüllermeier, Willem Waegeman
NeurIPS 2021 Credal Self-Supervised Learning Julian Lienen, Eyke Hüllermeier
AAAI 2021 From Label Smoothing to Label Relaxation Julian Lienen, Eyke Hüllermeier
ECML-PKDD 2021 Gradient-Based Label Binning in Multi-Label Classification Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier
NeurIPS 2021 Identification of the Generalized Condorcet Winner in Multi-Dueling Bandits Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier
CVPR 2021 Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model Julian Lienen, Eyke Hullermeier, Ralph Ewerth, Nils Nommensen
JAIR 2021 Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction Under Label Independence Vu-Linh Nguyen, Eyke Hüllermeier
MLJ 2021 On Testing Transitivity in Online Preference Learning Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier
JMLR 2021 Preference-Based Online Learning with Dueling Bandits: A Survey Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier
ACML 2021 Robust Regression for Monocular Depth Estimation Julian Lienen, Nils Nommensen, Ralph Ewerth, Eyke Hüllermeier
AAAI 2021 Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model Felix Mohr, Viktor Bengs, Eyke Hüllermeier
UAI 2021 Testification of Condorcet Winners in Dueling Bandits Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier
ACML 2020 A Novel Higher-Order Weisfeiler-Lehman Graph Convolution Clemens Damke, Vitalik Melnikov, Eyke Hüllermeier
MLJ 2020 Introduction to the Special Issue of the ECML PKDD 2020 Journal Track Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier
ECML-PKDD 2020 Learning Gradient Boosted Multi-Label Classification Rules Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier
IJCAI 2020 Neural Representation and Learning of Hierarchical 2-Additive Choquet Integrals Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag
ICML 2020 Preselection Bandits Viktor Bengs, Eyke Hüllermeier
AAAI 2020 Reliable Multilabel Classification: Prediction with Partial Abstention Vu-Linh Nguyen, Eyke Hüllermeier
ACML 2020 Run2Survive: A Decision-Theoretic Approach to Algorithm Selection Based on Survival Analysis Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier
ECML-PKDD 2019 A Reduction of Label Ranking to Multiclass Classification Klaus Brinker, Eyke Hüllermeier
ACML 2019 Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA Vitalik Melnikov, Eyke Hüllermeier
MLJ 2018 Dyad Ranking Using Plackett-Luce Models Based on Joint Feature Representations Dirk Schäfer, Eyke Hüllermeier
AAAI 2018 Learning to Rank Based on Analogical Reasoning Mohsen Ahmadi Fahandar, Eyke Hüllermeier
MLJ 2018 ML-Plan: Automated Machine Learning via Hierarchical Planning Felix Mohr, Marcel Wever, Eyke Hüllermeier
MLJ 2018 On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis Vitalik Melnikov, Eyke Hüllermeier
ICML 2018 Ranking Distributions Based on Noisy Sorting Adil El Mesaoudi-Paul, Eyke Hüllermeier, Robert Busa-Fekete
IJCAI 2018 Reliable Multi-Class Classification Based on Pairwise Epistemic and Aleatoric Uncertainty Vu-Linh Nguyen, Sébastien Destercke, Marie-Hélène Masson, Eyke Hüllermeier
ECML-PKDD 2017 Learning TSK Fuzzy Rules from Data Streams Ammar Shaker, Waleri Heldt, Eyke Hüllermeier
ICML 2017 Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso
ECML-PKDD 2016 Consistency of Probabilistic Classifier Trees Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier
ICML 2016 Extreme F-Measure Maximization Using Sparse Probability Estimates Kalina Jasinska, Krzysztof Dembczynski, Robert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hullermeier
ECML-PKDD 2016 Learning to Aggregate Using Uninorms Vitalik Melnikov, Eyke Hüllermeier
ECML-PKDD 2015 Dyad Ranking Using a Bilinear Plackett-Luce Model Dirk Schäfer, Eyke Hüllermeier
NeurIPS 2015 Online F-Measure Optimization Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier
NeurIPS 2015 Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier
ICML 2015 Qualitative Multi-Armed Bandits: A Quantile-Based Approach Balazs Szorenyi, Robert Busa-Fekete, Paul Weng, Eyke Hüllermeier
ECML-PKDD 2015 Superset Learning Based on Generalized Loss Minimization Eyke Hüllermeier, Weiwei Cheng
ECML-PKDD 2015 Weighted Rank Correlation: A Flexible Approach Based on Fuzzy Order Relations Sascha Henzgen, Eyke Hüllermeier
ALT 2014 A Survey of Preference-Based Online Learning with Bandit Algorithms Róbert Busa-Fekete, Eyke Hüllermeier
MLJ 2014 Guest Editors' Introduction: Special Issue of the ECML/PKDD 2014 Journal Track Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo
ECML-PKDD 2014 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo
ECML-PKDD 2014 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo
ECML-PKDD 2014 Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo
JMLR 2014 On the Bayes-Optimality of F-Measure Maximizers Willem Waegeman, Krzysztof Dembczyński, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier
AAAI 2014 PAC Rank Elicitation Through Adaptive Sampling of Stochastic Pairwise Preferences Róbert Busa-Fekete, Balázs Szörényi, Eyke Hüllermeier
MLJ 2014 Preference-Based Reinforcement Learning: Evolutionary Direct Policy Search Using a Preference-Based Racing Algorithm Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Weiwei Cheng, Eyke Hüllermeier
MLJ 2013 Editorial: Preference Learning and Ranking Eyke Hüllermeier, Johannes Fürnkranz
IJCAI 2013 Preference-Based CBR: General Ideas and Basic Principles Eyke Hüllermeier, Weiwei Cheng
ICML 2012 Consistent Multilabel Ranking Through Univariate Losses Krzysztof Dembczynski, Wojciech Kotlowski, Eyke Hüllermeier
NeurIPS 2012 Label Ranking with Partial Abstention Based on Thresholded Probabilistic Models Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker
MLJ 2012 Learning Monotone Nonlinear Models Using the Choquet Integral Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier
MLJ 2012 On Label Dependence and Loss Minimization in Multi-Label Classification Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
MLJ 2012 Preference-Based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm Johannes Fürnkranz, Eyke Hüllermeier, Weiwei Cheng, Sang-Hyeun Park
ECML-PKDD 2012 Probability Estimation for Multi-Class Classification Based on Label Ranking Weiwei Cheng, Eyke Hüllermeier
NeurIPS 2011 An Exact Algorithm for F-Measure Maximization Krzysztof J. Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
ICML 2011 Bipartite Ranking Through Minimization of Univariate Loss Wojciech Kotlowski, Krzysztof Dembczynski, Eyke Hüllermeier
ECML-PKDD 2011 Learning Monotone Nonlinear Models Using the Choquet Integral Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier
ALT 2011 Learning from Label Preferences Eyke Hüllermeier, Johannes Fürnkranz
ECML-PKDD 2011 Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning Weiwei Cheng, Johannes Fürnkranz, Eyke Hüllermeier, Sang-Hyeun Park
ICML 2010 Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier
ICML 2010 Graded Multilabel Classification: The Ordinal Case Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier
ICML 2010 Label Ranking Methods Based on the Plackett-Luce Model Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier
ECML-PKDD 2010 Predicting Partial Orders: Ranking with Abstention Weiwei Cheng, Michaël Rademaker, Bernard De Baets, Eyke Hüllermeier
ECML-PKDD 2010 Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
ECML-PKDD 2009 Binary Decomposition Methods for Multipartite Ranking Johannes Fürnkranz, Eyke Hüllermeier, Stijn Vanderlooy
ECML-PKDD 2009 Combining Instance-Based Learning and Logistic Regression for Multilabel Classification Weiwei Cheng, Eyke Hüllermeier
MLJ 2009 Combining Instance-Based Learning and Logistic Regression for Multilabel Classification Weiwei Cheng, Eyke Hüllermeier
ICML 2009 Decision Tree and Instance-Based Learning for Label Ranking Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier
ECML-PKDD 2008 A Critical Analysis of Variants of the AUC Stijn Vanderlooy, Eyke Hüllermeier
MLJ 2008 A Critical Analysis of Variants of the AUC Stijn Vanderlooy, Eyke Hüllermeier
MLJ 2008 Multilabel Classification via Calibrated Label Ranking Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker
IJCAI 2007 Case-Based Multilabel Ranking Klaus Brinker, Eyke Hüllermeier
ECML-PKDD 2006 Case-Based Label Ranking Klaus Brinker, Eyke Hüllermeier
IJCAI 2005 Cho-K-NN: A Method for Combining Interacting Pieces of Evidence in Case-Based Learning Eyke Hüllermeier
ECML-PKDD 2003 Pairwise Preference Learning and Ranking Johannes Fürnkranz, Eyke Hüllermeier
ECML-PKDD 2002 Possibilistic Induction in Decision-Tree Learning Eyke Hüllermeier
AAAI 2000 Change Detection in Heuristic Search Eyke Hüllermeier
IJCAI 1999 Toward a Probabilistic Formalization of Case-Based Inference Eyke Hüllermeier