| 2026 | AAAI | Uncertainty Quantification for Machine Learning: One Size Does Not Fit All. | Paul Hofman, Yusuf Sale, Eyke Hllermeier |
| 2026 | AAAI | Shapley Value Approximation Based on k-Additive Games. | Guilherme Dean Pelegrina, Patrick Kolpaczki, Eyke Hllermeier |
| 2026 | AAAI | Fine-grained Uncertainty Decomposition in Large Language Models: A Spectral Approach. | Nassim Walha, Sebastian G. Gruber, Thomas Decker, Yinchong Yang, Alireza Javanmardi, Eyke Hllermeier, Florian Buettner |
| 2026 | GECCO | Evolutionary Mapping of Neural Networks to Spatial Accelerators. | Alessandro Pierro, Jason Yik, Jonathan Timcheck, Marius Lindauer, Eyke Hllermeier, Marcel Wever |
| 2025 | AAAI | DUO: Diverse, Uncertain, On-Policy Query Generation and Selection for Reinforcement Learning from Human Feedback. | Xuening Feng, Zhaohui Jiang, Timo Kaufmann, Puchen Xu, Eyke Hllermeier, Paul Weng, Yifei Zhu |
| 2025 | AISTATS | Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory. | Fabian Fumagalli, Maximilian Muschalik, Eyke Hllermeier, Barbara Hammer, Julia Herbinger |
| 2025 | EMNLP | Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions. | Meghdut Sengupta, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hllermeier, Debanjan Ghosh, Henning Wachsmuth |
| 2025 | ESANN | Explaining Outliers using Isolation Forest and Shapley Interactions. | Roel Visser, Fabian Fumagalli, Maximilian Muschalik, Eyke Hllermeier, Barbara Hammer |
| 2025 | GECCO | SynthACticBench: A Capability-Based Synthetic Benchmark for Algorithm Configuration. | Valentin Margraf, Anna Lappe, Marcel Wever, Carolin Benjamins, Eyke Hllermeier, Marius Lindauer |
| 2025 | ICLR | Inverse Constitutional AI: Compressing Preferences into Principles. | Arduin Findeis, Timo Kaufmann, Eyke Hllermeier, Samuel Albanie, Robert Mullins |
| 2025 | ICLR | Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks. | Maximilian Muschalik, Fabian Fumagalli, Paolo Frazzetto, Janine Strotherm, Luca Hermes, Alessandro Sperduti, Eyke Hllermeier, Barbara Hammer |
| 2025 | ICML | Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries. | Xuening Feng, Zhaohui Jiang, Timo Kaufmann, Eyke Hllermeier, Paul Weng, Yifei Zhu |
| 2025 | ICML | X-Hacking: The Threat of Misguided AutoML. | Rahul Sharma, Sumantrak Mukherjee, Andrea Sipka, Eyke Hllermeier, Sebastian Josef Vollmer, Sergey Redyuk, David Antony Selby |
| 2025 | NAACL | Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection. | Maximilian Spliethver, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik, Barbara Hammer, Eyke Hllermeier, Henning Wachsmuth |
| 2025 | UAI | Conformal Prediction without Nonconformity Scores. | Jonas Hanselle, Alireza Javanmardi, Tobias Florin Oberkofler, Yusuf Sale, Eyke Hllermeier |
| 2025 | SIGdial | Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues. | Leandra Fichtel, Maximilian Spliethver, Eyke Hllermeier, Patricia Jimenez, Nils Oliver Klowait, Stefan Kopp, Axel-Cyrille Ngonga Ngomo, Amelie Sophie Robrecht, Ingrid Scharlau, Lutz Terfloth, Anna-Lisa Vollmer, Henning Wachsmuth |
| 2024 | AAAI | Approximating the Shapley Value without Marginal Contributions. | Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hllermeier |
| 2024 | AAAI | Mitigating Label Noise through Data Ambiguation. | Julian Lienen, Eyke Hllermeier |
| 2024 | AAAI | Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. | Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hllermeier |
| 2024 | AIME | Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. | Timo Lhr, Michael Ingrisch, Eyke Hllermeier |
| 2024 | AISTATS | Identifying Copeland Winners in Dueling Bandits with Indifferences. | Viktor Bengs, Bjrn Haddenhorst, Eyke Hllermeier |
| 2024 | AISTATS | SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. | Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hllermeier |
| 2024 | BMVC | Unsupervised Class Incremental Learning using Empty Classes. | Svenja Uhlemeyer, Julian Lienen, Youssef Shoeb, Eyke Hllermeier, Hanno Gottschalk |
| 2024 | DIS | Pairwise Difference Learning for Classification. | Mohamed Karim Belaid, Maximilian Rabus, Eyke Hllermeier |
| 2024 | ICLR | Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization. | Amirhossein Vahidi, Simon Schoer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hllermeier, Mina Rezaei |
| 2024 | ICML | KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. | Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hllermeier, Barbara Hammer |
| 2024 | ICML | 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 Rgamer, Eyke Hllermeier, Anne-Laure Boulesteix, Bernd Bischl |
| 2024 | ICML | Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? | Mira Jrgens, Nis Meinert, Viktor Bengs, Eyke Hllermeier, Willem Waegeman |
| 2024 | ICML | Second-Order Uncertainty Quantification: A Distance-Based Approach. | Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hllermeier |
| 2024 | IJCAI | Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. | Jasmin Brandt, Marcel Wever, Viktor Bengs, Eyke Hllermeier |
| 2024 | UAI | Linear Opinion Pooling for Uncertainty Quantification on Graphs. | Clemens Damke, Eyke Hllermeier |
| 2024 | UAI | Label-wise Aleatoric and Epistemic Uncertainty Quantification. | Yusuf Sale, Paul Hofman, Timo Lhr, Lisa Wimmer, Thomas Nagler, Eyke Hllermeier |
| 2023 | AAAI | AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. | Jasmin Brandt, Elias Schede, Bjrn Haddenhorst, Viktor Bengs, Eyke Hllermeier, Kevin Tierney |
| 2023 | AISTATS | On the Calibration of Probabilistic Classifier Sets. | Thomas Mortier, Viktor Bengs, Eyke Hllermeier, Stijn Luca, Willem Waegeman |
| 2023 | DIS | Probabilistic Scoring Lists for Interpretable Machine Learning. | Jonas Hanselle, Johannes Frnkranz, Eyke Hllermeier |
| 2023 | ESANN | On Feature Removal for Explainability in Dynamic Environments. | Fabian Fumagalli, Maximilian Muschalik, Eyke Hllermeier, Barbara Hammer |
| 2023 | GECCO | Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. | Marcel Wever, Miran zdogan, Eyke Hllermeier |
| 2023 | ICLR | Memorization-Dilation: Modeling Neural Collapse Under Noise. | Duc Anh Nguyen, Ron Levie, Julian Lienen, Eyke Hllermeier, Gitta Kutyniok |
| 2023 | ICML | On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. | Viktor Bengs, Eyke Hllermeier, Willem Waegeman |
| 2023 | IJCAI | A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). | Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hllermeier, Kevin Tierney |
| 2023 | MSR | UnGoML: Automated Classification of unsafe Usages in Go. | Anna-Katharina Wickert, Clemens Damke, Lars Baumgrtner, Eyke Hllermeier, Mira Mezini |
| 2023 | UAI | Is the volume of a credal set a good measure for epistemic uncertainty? | Yusuf Sale, Michele Caprio, Eyke Hllermeier |
| 2023 | UAI | 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 Hllermeier |
| 2022 | AAAI | Machine Learning for Online Algorithm Selection under Censored Feedback. | Alexander Tornede, Viktor Bengs, Eyke Hllermeier |
| 2022 | ICAART | Automated Information Leakage Detection: A New Method Combining Machine Learning and Hypothesis Testing with an Application to Side-channel Detection in Cryptographic Protocols. | Pritha Gupta, Arunselvan Ramaswamy, Jan Peter Drees, Eyke Hllermeier, Claudia Priesterjahn, Tibor Jager |
| 2022 | ICML | Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. | Viktor Bengs, Aadirupa Saha, Eyke Hllermeier |
| 2022 | UAI | Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison. | Eyke Hllermeier, Sbastien Destercke, Mohammad Hossein Shaker |
| 2022 | UAI | Set-valued prediction in hierarchical classification with constrained representation complexity. | Thomas Mortier, Eyke Hllermeier, Krzysztof Dembczynski, Willem Waegeman |
| 2021 | AAAI | From Label Smoothing to Label Relaxation. | Julian Lienen, Eyke Hllermeier |
| 2021 | AAAI | Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. | Felix Mohr, Viktor Bengs, Eyke Hllermeier |
| 2021 | ACML | Robust Regression for Monocular Depth Estimation. | Julian Lienen, Nils Nommensen, Ralph Ewerth, Eyke Hllermeier |
| 2021 | CCS | Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! | Jan Peter Drees, Pritha Gupta, Eyke Hllermeier, Tibor Jager, Alexander Konze, Claudia Priesterjahn, Arunselvan Ramaswamy, Juraj Somorovsky |
| 2021 | CVPR | Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model. | Julian Lienen, Eyke Hllermeier, Ralph Ewerth, Nils Nommensen |
| 2021 | DIS | Ranking Structured Objects with Graph Neural Networks. | Clemens Damke, Eyke Hllermeier |
| 2021 | GECCO | Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. | Tanja Tornede, Alexander Tornede, Marcel Wever, Eyke Hllermeier |
| 2021 | IDA | Analogical Embedding for Analogy-Based Learning to Rank. | Mohsen Ahmadi Fahandar, Eyke Hllermeier |
| 2021 | IDA | Performance Prediction for Hardware-Software Configurations: A Case Study for Video Games. | Sven Peeters, Vitalik Melnikov, Eyke Hllermeier |
| 2021 | IDEAL | Drift Detection in Text Data with Document Embeddings. | Robert Feldhans, Adrian Wilke, Stefan Heindorf, Mohammad Hossein Shaker, Barbara Hammer, Axel-Cyrille Ngonga Ngomo, Eyke Hllermeier |
| 2021 | KR | On the Identifiability of Hierarchical Decision Models. | Roman Bresson, Johanne Cohen, Eyke Hllermeier, Christophe Labreuche, Michle Sebag |
| 2021 | PAKDD | Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. | Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hllermeier |
| 2021 | UAI | Testification of Condorcet Winners in dueling bandits. | Bjrn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hllermeier |
| 2020 | AAAI | Reliable Multilabel Classification: Prediction with Partial Abstention. | Vu-Linh Nguyen, Eyke Hllermeier |
| 2020 | ACML | A Novel Higher-order Weisfeiler-Lehman Graph Convolution. | Clemens Damke, Vitalik Melnikov, Eyke Hllermeier |
| 2020 | ACML | Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. | Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hllermeier |
| 2020 | DIS | On Aggregation in Ensembles of Multilabel Classifiers. | Vu-Linh Nguyen, Eyke Hllermeier, Michael Rapp, Eneldo Loza Menca, Johannes Frnkranz |
| 2020 | DIS | Extreme Algorithm Selection with Dyadic Feature Representation. | Alexander Tornede, Marcel Wever, Eyke Hllermeier |
| 2020 | GI | The Dissimilar in the Similar. An Attribute-guided Approach to the Subject-specific Classification of Art-historical Objects. | Stefanie Schneider, Matthias Springstein, Javad Rahnama, Eyke Hllermeier, Ralph Ewerth, Hubertus Kohle |
| 2020 | ICML | Preselection Bandits. | Viktor Bengs, Eyke Hllermeier |
| 2020 | IJCAI | Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. | Roman Bresson, Johanne Cohen, Eyke Hllermeier, Christophe Labreuche, Michle Sebag |
| 2020 | IJCNN | A Neural Network-Based Driver Gaze Classification System with Vehicle Signals. | Simone Dari, Nikolay Kadrileev, Eyke Hllermeier |
| 2020 | IPMU | Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. | Andrea Campagner, Davide Ciucci, Eyke Hllermeier |
| 2020 | IPMU | Learning Tversky Similarity. | Javad Rahnama, Eyke Hllermeier |
| 2020 | IDA | Aleatoric and Epistemic Uncertainty with Random Forests. | Mohammad Hossein Shaker, Eyke Hllermeier |
| 2020 | IDA | LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. | Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hllermeier |
| 2020 | KI | Hybrid Ranking and Regression for Algorithm Selection. | Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hllermeier |
| 2020 | KI | Conformal Rule-Based Multi-label Classification. | Eyke Hllermeier, Johannes Frnkranz, Eneldo Loza Menca |
| 2020 | KI | Learning Choice Functions via Pareto-Embeddings. | Karlson Pfannschmidt, Eyke Hllermeier |
| 2020 | MDAI | Towards Analogy-Based Explanations in Machine Learning. | Eyke Hllermeier |
| 2019 | ACML | Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. | Vitalik Melnikov, Eyke Hllermeier |
| 2019 | DIS | Feature Selection for Analogy-Based Learning to Rank. | Mohsen Ahmadi Fahandar, Eyke Hllermeier |
| 2019 | DIS | Epistemic Uncertainty Sampling. | Vu-Linh Nguyen, Sbastien Destercke, Eyke Hllermeier |
| 2019 | GI | From Automated to On-The-Fly Machine Learning. | Felix Mohr, Marcel Wever, Alexander Tornede, Eyke Hllermeier |
| 2019 | KI | Analogy-Based Preference Learning with Kernels. | Mohsen Ahmadi Fahandar, Eyke Hllermeier |
| 2018 | AAAI | Learning to Rank Based on Analogical Reasoning. | Mohsen Ahmadi Fahandar, Eyke Hllermeier |
| 2018 | DIS | Preference-Based Reinforcement Learning Using Dyad Ranking. | Dirk Schfer, Eyke Hllermeier |
| 2018 | GECCO | Ensembles of evolved nested dichotomies for classification. | Marcel Wever, Felix Mohr, Eyke Hllermeier |
| 2018 | ICML | Ranking Distributions based on Noisy Sorting. | Adil El Mesaoudi-Paul, Eyke Hllermeier, Rbert Busa-Fekete |
| 2018 | IJCAI | Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. | Vu-Linh Nguyen, Sbastien Destercke, Marie-Hlne Masson, Eyke Hllermeier |
| 2018 | IDA | Reduction Stumps for Multi-class Classification. | Felix Mohr, Marcel Wever, Eyke Hllermeier |
| 2017 | CoDIT | Keynote 4: "Large-scale machine learning and extreme classification". | Eyke Hllermeier |
| 2017 | ICML | Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. | Mohsen Ahmadi Fahandar, Eyke Hllermeier, Ins Couso |
| 2017 | KI | Planning with Independent Task Networks. | Felix Mohr, Theo Lettmann, Eyke Hllermeier |
| 2016 | ICCBR | Predicting the Electricity Consumption of Buildings: An Improved CBR Approach. | Aulon Shabani, Adil Paul, Radu Platon, Eyke Hllermeier |
| 2016 | ICML | Extreme F-measure Maximization using Sparse Probability Estimates. | Kalina Jasinska, Krzysztof Dembczynski, Rbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hllermeier |
| 2016 | IPMU | Evaluating Tests in Medical Diagnosis: Combining Machine Learning with Game-Theoretical Concepts. | Karlson Pfannschmidt, Eyke Hllermeier, Susanne Held, Reto Neiger |
| 2015 | ICCBR | Case Base Maintenance in Preference-Based CBR. | Amira Abdel-Aziz, Eyke Hllermeier |
| 2015 | ICCBR | A CBR Approach to the Angry Birds Game. | Adil Paul, Eyke Hllermeier |
| 2015 | ICML | Qualitative Multi-Armed Bandits: A Quantile-Based Approach. | Balzs Szrnyi, Rbert Busa-Fekete, Paul Weng, Eyke Hllermeier |
| 2014 | AAAI | PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. | Rbert Busa-Fekete, Balzs Szrnyi, Eyke Hllermeier |
| 2014 | ALT | A Survey of Preference-Based Online Learning with Bandit Algorithms. | Rbert Busa-Fekete, Eyke Hllermeier |
| 2014 | DIS | Mining Rank Data. | Sascha Henzgen, Eyke Hllermeier |
| 2014 | ESANN | The Choquet kernel for monotone data. | Ali Fallah Tehrani, Marc Strickert, Eyke Hllermeier |
| 2014 | ICCBR | Learning Solution Similarity in Preference-Based CBR. | Amira Abdel-Aziz, Marc Strickert, Eyke Hllermeier |
| 2014 | ICML | Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. | Rbert Busa-Fekete, Eyke Hllermeier, Balzs Szrnyi |
| 2013 | EMNLP | Learning to Rank Lexical Substitutions. | Gyrgy Szarvas, Rbert Busa-Fekete, Eyke Hllermeier |
| 2013 | EUSFLAT | Ordinal Choquistic Regression. | Ali Fallah Tehrani, Eyke Hllermeier |
| 2013 | ICCBR | Preference-Based CBR: A Search-Based Problem Solving Framework. | Amira Abdel-Aziz, Weiwei Cheng, Marc Strickert, Eyke Hllermeier |
| 2013 | ICML | Top-k Selection based on Adaptive Sampling of Noisy Preferences. | Rbert Busa-Fekete, Balzs Szrnyi, Weiwei Cheng, Paul Weng, Eyke Hllermeier |
| 2013 | ICML | Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. | Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hllermeier |
| 2013 | IJCAI | Preference-Based CBR: General Ideas and Basic Principles. | Eyke Hllermeier, Weiwei Cheng |
| 2013 | IJCCI | Learning from Imprecise and Fuzzy Data: on the Notion of Data Disambiguation. | Eyke Hllermeier |
| 2013 | IFSA | Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing. | Maryam Nasiri, Thomas Fober, Robin Senge, Eyke Hllermeier |
| 2012 | ECAI | An Analysis of Chaining in Multi-Label Classification. | Krzysztof Dembczynski, Willem Waegeman, Eyke Hllermeier |
| 2012 | ICML | Consistent Multilabel Ranking through Univariate Losses. | Krzysztof Dembczynski, Wojciech Kotlowski, Eyke Hllermeier |
| 2012 | IPMU | On the VC-Dimension of the Choquet Integral. | Eyke Hllermeier, Ali Fallah Tehrani |
| 2011 | ALT | Learning from Label Preferences. | Eyke Hllermeier, Johannes Frnkranz |
| 2011 | DIS | Learning from Label Preferences. | Eyke Hllermeier, Johannes Frnkranz |
| 2011 | EUSFLAT | On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure. | Edwin Lughofer, Eyke Hllermeier |
| 2011 | EUSFLAT | Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral. | Ali Fallah Tehrani, Weiwei Cheng, Eyke Hllermeier |
| 2011 | ICCBR | Preference-Based CBR: First Steps toward a Methodological Framework. | Eyke Hllermeier, Patrice Schlegel |
| 2011 | ICML | Bipartite Ranking through Minimization of Univariate Loss. | Wojciech Kotlowski, Krzysztof Dembczynski, Eyke Hllermeier |
| 2010 | ICML | Label Ranking Methods based on the Plackett-Luce Model. | Weiwei Cheng, Krzysztof Dembczynski, Eyke Hllermeier |
| 2010 | ICML | Graded Multilabel Classification: The Ordinal Case. | Weiwei Cheng, Krzysztof Dembczynski, Eyke Hllermeier |
| 2010 | ICML | Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains. | Krzysztof Dembczynski, Weiwei Cheng, Eyke Hllermeier |
| 2009 | EUSFLAT | Fuzzy Modeling of Labeled Point Cloud Superposition for the Comparison of Protein Binding Sites. | Thomas Fober, Eyke Hllermeier |
| 2009 | EUSFLAT | A Fuzzy Variant of the Rand Index for Comparing Clustering Structures. | Eyke Hllermeier, Maria Rifqi |
| 2009 | ICML | Decision tree and instance-based learning for label ranking. | Weiwei Cheng, Jens C. Huhn, Eyke Hllermeier |
| 2009 | ISDA | Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection. | Imen Boukhris, Zied Elouedi, Thomas Fober, Marco Mernberger, Eyke Hllermeier |
| 2009 | ISDA | Efficient Construction of Multiple Geometrical Alignments for the Comparison of Protein Binding Sites. | Thomas Fober, Gerhard Klebe, Eyke Hllermeier |
| 2009 | ISNN | A New Instance-Based Label Ranking Approach Using the Mallows Model. | Weiwei Cheng, Eyke Hllermeier |
| 2007 | EUSFLAT | Fuzzy-Relational Classification: Combining Pairwise Decomposition Techniques with Fuzzy Preference Modeling. | Eyke Hllermeier, Klaus Brinker |
| 2007 | ICCBR | Label Ranking in Case-Based Reasoning. | Klaus Brinker, Eyke Hllermeier |
| 2007 | IJCAI | Case-Based Multilabel Ranking. | Klaus Brinker, Eyke Hllermeier |
| 2006 | ECAI | A Unified Model for Multilabel Classification and Ranking. | Klaus Brinker, Johannes Frnkranz, Eyke Hllermeier |
| 2006 | ICDM | Hierarchical Classification by Expected Utility Maximization. | Korinna Bade, Eyke Hllermeier, Andreas Nrnberger |
| 2005 | ECSQARU | A Notion of Comparative Probabilistic Entropy Based on the Possibilistic Specificity Ordering. | Didier Dubois, Eyke Hllermeier |
| 2005 | EUSFLAT | Improving the interpretability of data-driven evolving fuzzy systems. | Edwin Lughofer, Eyke Hllermeier, Erich-Peter Klement |
| 2005 | EUSFLAT | Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms. | Yu Yi, Eyke Hllermeier |
| 2005 | IJCAI | Cho-k-NN: A Method for Combining Interacting Pieces of Evidence in Case-Based Learning. | Eyke Hllermeier |
| 2005 | IDA | Learning from Ambiguously Labeled Examples. | Eyke Hllermeier, Jrgen Beringer |
| 2005 | IDA | Learning Label Preferences: Ranking Error Versus Position Error. | Eyke Hllermeier, Johannes Frnkranz |
| 2004 | ECAI | Instance-Based Prediction with Guaranteed Confidence. | Eyke Hllermeier |
| 2003 | EUSFLAT | Instance-based collaborative filtering with fuzzy labels. | Eyke Hllermeier |
| 2003 | IDA | Regularized Learning with Flexible Constraints. | Eyke Hllermeier |
| 2003 | IFSA | A Note on Quality Measures for Fuzzy Asscociation Rules. | Didier Dubois, Eyke Hllermeier, Henri Prade |
| 2003 | IFSA | Inducing Fuzzy Concepts through Extended Version Space Learning. | Eyke Hllermeier |
| 2003 | KI | Instance-Based Learning of Credible Label Sets. | Eyke Hllermeier |
| 2002 | ECAI | On the Representation and Combination of Evidence in Instance-Based Learning. | Eyke Hllermeier |
| 2002 | KR | A Fuzzy Approach to Flexible Case-based Querying: Methodology and Experimentation. | Martine de Calms, Didier Dubois, Eyke Hllermeier, Henri Prade, Florence Sdes |
| 2000 | AAAI | Change Detection in Heuristic Search. | Eyke Hllermeier |
| 2000 | ECAI | Focusing Search by Using Problem Solving Experience. | Eyke Hllermeier |
| 2000 | ECAI | Similarity-based Inference as Evitential Reasoning. | Eyke Hllermeier |
| 1999 | IJCAI | Toward a Probabilistic Formalization of Case-Based Inference. | Eyke Hllermeier |
| 1999 | IDA | Exploiting Similarity for Supporting Data Analysis and Problem Solving. | Eyke Hllermeier |