Eyke Hüllermeier
168 publications
24 venues
H Index 42
Affiliation
LMU Munich, GermanyPaderborn University, Germany
Links
DBLPGoogle Scholar
LinkedIn- https://www.kiml.ifi.lmu.de/people/professors/huellermeier/index.html
- https://www.uni-paderborn.de/person/48129/
- https://openreview.net/profile?id=~Eyke_H%C3%BCllermeier1
- https://zbmath.org/authors/?q=ai:hullermeier.eyke
- https://orcid.org/0000-0002-9944-4108
- https://www.wikidata.org/entity/Q30828813
- https://id.loc.gov/authorities/names/nb2007009670
- https://d-nb.info/gnd/115498370
| Name | Venue | Year | citations |
|---|---|---|---|
Uncertainty Quantification for Machine Learning: One Size Does Not Fit All.
|
AAAI | 2026 | 0 |
| Fine-grained Uncertainty Decomposition in Large Language Models: A Spectral Approach.
|
AAAI | 2026 | 0 |
| Uncertainty Quantification in Pairwise Difference Learning for Classification.
|
MLJ | 2026 | 0 |
| X-Hacking: The Threat of Misguided AutoML.
|
ICML | 2025 | 0 |
| Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks.
|
ICLR | 2025 | 7 |
| A calibration test for evaluating set-based epistemic uncertainty representations.
|
MLJ | 2025 | 4 |
| Conformal Prediction without Nonconformity Scores.
|
UAI | 2025 | 1 |
| Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries.
|
ICML | 2025 | 1 |
| Distribution Matching for Graph Quantification Under Structural Covariate Shift.
|
ECML/PKDD | 2025 | 0 |
| DUO: Diverse, Uncertain, On-Policy Query Generation and Selection for Reinforcement Learning from Human Feedback.
|
AAAI | 2025 | 6 |
| Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration for Exosuit Personalization.
|
ECML/PKDD | 2025 | 0 |
| Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory.
|
AISTATS | 2025 | 0 |
| Inverse Constitutional AI: Compressing Preferences into Principles.
|
ICLR | 2025 | 0 |
| Probabilistic scoring lists for interpretable machine learning.
|
MLJ | 2025 | 0 |
| Position: Why We Must Rethink Empirical Research in Machine Learning.
|
ICML | 2024 | 25 |
| shapiq: Shapley Interactions for Machine Learning.
|
NIPS/NeurIPS | 2024 | 40 |
| CUQ-GNN: Committee-Based Graph Uncertainty Quantification Using Posterior Networks.
|
ECML/PKDD | 2024 | 0 |
| Conformalized Credal Set Predictors.
|
NIPS/NeurIPS | 2024 | 20 |
| Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
|
AAAI | 2024 | 32 |
| Linear Opinion Pooling for Uncertainty Quantification on Graphs.
|
UAI | 2024 | 2 |
| Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization.
|
ICLR | 2024 | 6 |
| SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification.
|
AISTATS | 2024 | 14 |
| Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
|
ICML | 2024 | 31 |
| Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO.
|
IJCAI | 2024 | 1 |
| Label-wise Aleatoric and Epistemic Uncertainty Quantification.
|
UAI | 2024 | 18 |
| Approximating the Shapley Value without Marginal Contributions.
|
AAAI | 2024 | 0 |
| Mitigating Label Noise through Data Ambiguation.
|
AAAI | 2024 | 0 |
| Second-Order Uncertainty Quantification: A Distance-Based Approach.
|
ICML | 2024 | 0 |
| KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions.
|
ICML | 2024 | 0 |
| Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning.
|
ECML/PKDD | 2024 | 0 |
| Identifying Copeland Winners in Dueling Bandits with Indifferences.
|
AISTATS | 2024 | 0 |
| On Second-Order Scoring Rules for Epistemic Uncertainty Quantification.
|
ICML | 2023 | 34 |
| On Feature Removal for Explainability in Dynamic Environments.
|
ESANN | 2023 | 0 |
| A Survey of Methods for Automated Algorithm Configuration (Extended Abstract).
|
IJCAI | 2023 | 0 |
| iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams.
|
ECML/PKDD | 2023 | 6 |
| SHAP-IQ: Unified Approximation of any-order Shapley Interactions.
|
NIPS/NeurIPS | 2023 | 52 |
| Memorization-Dilation: Modeling Neural Collapse Under Noise.
|
ICLR | 2023 | 15 |
| Koopman Kernel Regression.
|
NIPS/NeurIPS | 2023 | 27 |
| Is the volume of a credal set a good measure for epistemic uncertainty?
|
UAI | 2023 | 38 |
| Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing.
|
ECML/PKDD | 2023 | 3 |
| AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration.
|
AAAI | 2023 | 0 |
| Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures?
|
UAI | 2023 | 0 |
| On the Calibration of Probabilistic Classifier Sets.
|
AISTATS | 2023 | 0 |
| Multi-armed bandits with censored consumption of resources.
|
MLJ | 2023 | 0 |
| Algorithm selection on a meta level.
|
MLJ | 2023 | 0 |
| Incremental permutation feature importance (iPFI): towards online explanations on data streams.
|
MLJ | 2023 | 0 |
| Towards Green Automated Machine Learning: Status Quo and Future Directions.
|
JAIR | 2023 | 0 |
| Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget.
|
NIPS/NeurIPS | 2022 | 8 |
| Set-valued prediction in hierarchical classification with constrained representation complexity.
|
UAI | 2022 | 5 |
| Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison.
|
UAI | 2022 | 48 |
| A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain.
|
ECML/PKDD | 2022 | 4 |
| A Survey of Methods for Automated Algorithm Configuration.
|
JAIR | 2022 | 70 |
| Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation.
|
NIPS/NeurIPS | 2022 | 55 |
| Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models.
|
ICML | 2022 | 30 |
| Machine Learning for Online Algorithm Selection under Censored Feedback.
|
AAAI | 2022 | 0 |
| How to measure uncertainty in uncertainty sampling for active learning.
|
MLJ | 2022 | 0 |
| A flexible class of dependence-aware multi-label loss functions.
|
MLJ | 2022 | 0 |
| Gradient-Based Label Binning in Multi-label Classification.
|
ECML/PKDD | 2021 | 6 |
| Robust Regression for Monocular Depth Estimation.
|
ACML | 2021 | 2 |
| Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data.
|
PAKDD | 2021 | 5 |
| TSK-Streams: learning TSK fuzzy systems for regression on data streams.
|
DMKD | 2021 | 2 |
| On testing transitivity in online preference learning.
|
MLJ | 2021 | 3 |
| Credal Self-Supervised Learning.
|
NIPS/NeurIPS | 2021 | 22 |
| Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model.
|
AAAI | 2021 | 2 |
| Identification of the Generalized Condorcet Winner in Multi-dueling Bandits.
|
NIPS/NeurIPS | 2021 | 9 |
| From Label Smoothing to Label Relaxation.
|
AAAI | 2021 | 65 |
| Testification of Condorcet Winners in dueling bandits.
|
UAI | 2021 | 4 |
| On the Identifiability of Hierarchical Decision Models.
|
KR | 2021 | 2 |
| Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence.
|
JAIR | 2021 | 12 |
| AutoML for Multi-Label Classification: Overview and Empirical Evaluation.
|
TPAMI | 2021 | 71 |
| Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
|
TPAMI | 2021 | 32 |
| Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model.
|
CVPR | 2021 | 0 |
| Efficient set-valued prediction in multi-class classification.
|
DMKD | 2021 | 0 |
| Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods.
|
MLJ | 2021 | 0 |
| Preference-based Online Learning with Dueling Bandits: A Survey.
|
JMLR | 2021 | 0 |
| Learning Gradient Boosted Multi-label Classification Rules.
|
ECML/PKDD | 2020 | 30 |
| Introduction to the special issue of the ECML PKDD 2020 journal track.
|
DMKD | 2020 | 0 |
| A Neural Network-Based Driver Gaze Classification System with Vehicle Signals.
|
IJCNN | 2020 | 13 |
| Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis.
|
ACML | 2020 | 13 |
| Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals.
|
IJCAI | 2020 | 17 |
| A Novel Higher-order Weisfeiler-Lehman Graph Convolution.
|
ACML | 2020 | 15 |
| Reliable Multilabel Classification: Prediction with Partial Abstention.
|
AAAI | 2020 | 0 |
| Preselection Bandits.
|
ICML | 2020 | 0 |
| Introduction to the special issue of the ECML PKDD 2020 journal track.
|
MLJ | 2020 | 0 |
| A Reduction of Label Ranking to Multiclass Classification.
|
ECML/PKDD | 2019 | 6 |
| Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA.
|
ACML | 2019 | 3 |
| Multi-target prediction: a unifying view on problems and methods.
|
DMKD | 2019 | 0 |
| On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis.
|
MLJ | 2018 | 24 |
| Ranking Distributions based on Noisy Sorting.
|
ICML | 2018 | 2 |
| ML-Plan: Automated machine learning via hierarchical planning.
|
MLJ | 2018 | 181 |
| Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty.
|
IJCAI | 2018 | 29 |
| Learning to Rank Based on Analogical Reasoning.
|
AAAI | 2018 | 0 |
| Dyad ranking using Plackett-Luce models based on joint feature representations.
|
MLJ | 2018 | 0 |
| Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening.
|
ICML | 2017 | 17 |
| Learning TSK Fuzzy Rules from Data Streams.
|
ECML/PKDD | 2017 | 3 |
| Extreme F-measure Maximization using Sparse Probability Estimates.
|
ICML | 2016 | 113 |
| Consistency of Probabilistic Classifier Trees.
|
ECML/PKDD | 2016 | 20 |
| Predicting the Electricity Consumption of Buildings: An Improved CBR Approach.
|
ICCBR | 2016 | 4 |
| Learning to Aggregate Using Uninorms.
|
ECML/PKDD | 2016 | 15 |
| Superset Learning Based on Generalized Loss Minimization.
|
ECML/PKDD | 2015 | 54 |
| Qualitative Multi-Armed Bandits: A Quantile-Based Approach.
|
ICML | 2015 | 49 |
| Online F-Measure Optimization.
|
NIPS/NeurIPS | 2015 | 38 |
| Dyad Ranking Using A Bilinear Plackett-Luce Model.
|
ECML/PKDD | 2015 | 16 |
| Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach.
|
NIPS/NeurIPS | 2015 | 83 |
| Weighted Rank Correlation: A Flexible Approach Based on Fuzzy Order Relations.
|
ECML/PKDD | 2015 | 8 |
| Case Base Maintenance in Preference-Based CBR.
|
ICCBR | 2015 | 5 |
| Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm.
|
MLJ | 2014 | 71 |
| Learning Solution Similarity in Preference-Based CBR.
|
ICCBR | 2014 | 11 |
| Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows.
|
ICML | 2014 | 69 |
| PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences.
|
AAAI | 2014 | 26 |
| The Choquet kernel for monotone data.
|
ESANN | 2014 | 6 |
| Guest editors' introduction: special issue of the ECML/PKDD 2014 journal track.
|
DMKD | 2014 | 0 |
| Guest Editors' introduction: special issue of the ECML/PKDD 2014 journal track.
|
MLJ | 2014 | 0 |
| On the bayes-optimality of F-measure maximizers.
|
JMLR | 2014 | 0 |
| Top-k Selection based on Adaptive Sampling of Noisy Preferences.
|
ICML | 2013 | 75 |
| Editorial: Preference learning and ranking.
|
MLJ | 2013 | 11 |
| Preference-Based CBR: General Ideas and Basic Principles.
|
IJCAI | 2013 | 5 |
| Preference-Based CBR: A Search-Based Problem Solving Framework.
|
ICCBR | 2013 | 8 |
| Learning to Rank Lexical Substitutions.
|
EMNLP | 2013 | 24 |
| Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization.
|
ICML | 2013 | 106 |
| On label dependence and loss minimization in multi-label classification.
|
MLJ | 2012 | 321 |
| Label Ranking with Partial Abstention based on Thresholded Probabilistic Models.
|
NIPS/NeurIPS | 2012 | 42 |
| Consistent Multilabel Ranking through Univariate Losses.
|
ICML | 2012 | 47 |
| Probability Estimation for Multi-class Classification Based on Label Ranking.
|
ECML/PKDD | 2012 | 7 |
| Preference-based reinforcement learning: a formal framework and a policy iteration algorithm.
|
MLJ | 2012 | 169 |
| An Analysis of Chaining in Multi-Label Classification.
|
ECAI | 2012 | 60 |
| Learning monotone nonlinear models using the Choquet integral.
|
MLJ | 2012 | 0 |
| Learning Monotone Nonlinear Models Using the Choquet Integral.
|
ECML/PKDD | 2011 | 147 |
| Preference-Based CBR: First Steps toward a Methodological Framework.
|
ICCBR | 2011 | 23 |
| An Exact Algorithm for F-Measure Maximization.
|
NIPS/NeurIPS | 2011 | 117 |
| Preferences in AI: An overview.
|
Artificial Intelligence | 2011 | 222 |
| Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning.
|
ECML/PKDD | 2011 | 65 |
| Bipartite Ranking through Minimization of Univariate Loss.
|
ICML | 2011 | 97 |
| Label Ranking Methods based on the Plackett-Luce Model.
|
ICML | 2010 | 108 |
| Graded Multilabel Classification: The Ordinal Case.
|
ICML | 2010 | 47 |
| Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss.
|
ECML/PKDD | 2010 | 61 |
| Predicting Partial Orders: Ranking with Abstention.
|
ECML/PKDD | 2010 | 70 |
| Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains.
|
ICML | 2010 | 501 |
| Binary Decomposition Methods for Multipartite Ranking.
|
ECML/PKDD | 2009 | 82 |
| FURIA: an algorithm for unordered fuzzy rule induction.
|
DMKD | 2009 | 462 |
| Decision tree and instance-based learning for label ranking.
|
ICML | 2009 | 148 |
| Combining Instance-Based Learning and Logistic Regression for Multilabel Classification.
|
ECML/PKDD | 2009 | 471 |
| Combining instance-based learning and logistic regression for multilabel classification.
|
MLJ | 2009 | 0 |
| Label ranking by learning pairwise preferences.
|
Artificial Intelligence | 2008 | 581 |
| A Critical Analysis of Variants of the AUC.
|
ECML/PKDD | 2008 | 56 |
| Multilabel classification via calibrated label ranking.
|
MLJ | 2008 | 938 |
| A critical analysis of variants of the AUC.
|
MLJ | 2008 | 0 |
| Case-Based Multilabel Ranking.
|
IJCAI | 2007 | 76 |
| On Minimizing the Position Error in Label Ranking.
|
ECML/PKDD | 2007 | 13 |
| On Pairwise Naive Bayes Classifiers.
|
ECML/PKDD | 2007 | 38 |
| Label Ranking in Case-Based Reasoning.
|
ICCBR | 2007 | 12 |
| An Efficient Algorithm for Instance-Based Learning on Data Streams.
|
ICDM | 2007 | 18 |
| A systematic approach to the assessment of fuzzy association rules.
|
DMKD | 2006 | 211 |
| A Unified Model for Multilabel Classification and Ranking.
|
ECAI | 2006 | 125 |
| Case-Based Label Ranking.
|
ECML/PKDD | 2006 | 25 |
| Hierarchical Classification by Expected Utility Maximization.
|
ICDM | 2006 | 5 |
| Cho-k-NN: A Method for Combining Interacting Pieces of Evidence in Case-Based Learning.
|
IJCAI | 2005 | 10 |
| Instance-Based Prediction with Guaranteed Confidence.
|
ECAI | 2004 | 5 |
| Possibilistic instance-based learning.
|
Artificial Intelligence | 2003 | 39 |
| Pairwise Preference Learning and Ranking.
|
ECML/PKDD | 2003 | 278 |
| On the Representation and Combination of Evidence in Instance-Based Learning.
|
ECAI | 2002 | 4 |
| Possibilistic Induction in Decision-Tree Learning.
|
ECML/PKDD | 2002 | 33 |
| A Fuzzy Approach to Flexible Case-based Querying: Methodology and Experimentation.
|
KR | 2002 | 13 |
| Association Rules for Expressing Gradual Dependencies.
|
ECML/PKDD | 2002 | 95 |
| Implication-Based Fuzzy Association Rules.
|
ECML/PKDD | 2001 | 60 |
| Focusing Search by Using Problem Solving Experience.
|
ECAI | 2000 | 5 |
| Similarity-based Inference as Evitential Reasoning.
|
ECAI | 2000 | 0 |
| Toward a Probabilistic Formalization of Case-Based Inference.
|
IJCAI | 1999 | 32 |