Eyke Hüllermeier

An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression UAI 2026 Fine-grained Uncertainty Decomposition in Large Language Models: A Spectral Approach AAAI 2026 Optimal Conformal Prediction under Epistemic Uncertainty UAI 2026 Quantification of Credal Uncertainty: A Distance-Based Approach UAI 2026 Reliable Conformal Prediction for Ordinal Classification Using the Ranked Probability Score UAI 2026 Shapley Value Approximation Based on k-Additive Games AAAI 2026 Structured Credal Learning UAI 2026 Uncertainty Quantification for Machine Learning: One Size Does Not Fit All AAAI 2026 Uncertainty Quantification for Regression: A Unified Framework based on kernel scores UAI 2026 Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection NAACL 2025 Comparing Comparisons: Informative and Easy Human Feedback with Distinguishability Queries ICML 2025 Conformal Prediction without Nonconformity Scores UAI 2025 DUO: Diverse, Uncertain, On-Policy Query Generation and Selection for Reinforcement Learning from Human Feedback AAAI 2025 Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks ICLR 2025 Inverse Constitutional AI: Compressing Preferences into Principles ICLR 2025 Investigating the Impact of Conceptual Metaphors on LLM-based NLI through Shapley Interactions EMNLP 2025 Unifying Feature-Based Explanations with Functional ANOVA and Cooperative Game Theory AISTATS 2025 X-Hacking: The Threat of Misguided AutoML ICML 2025 Approximating the Shapley Value without Marginal Contributions AAAI 2024 Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO IJCAI 2024 Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles AAAI 2024 Conformalized Credal Set Predictors NeurIPS 2024 Identifying Copeland Winners in Dueling Bandits with Indifferences AISTATS 2024 Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? ICML 2024 KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions ICML 2024 Label-wise Aleatoric and Epistemic Uncertainty Quantification UAI 2024 Linear Opinion Pooling for Uncertainty Quantification on Graphs UAI 2024 Mitigating Label Noise through Data Ambiguation AAAI 2024 Position: Why We Must Rethink Empirical Research in Machine Learning ICML 2024 Probabilistic Self-supervised Representation Learning via Scoring Rules Minimization ICLR 2024 Second-Order Uncertainty Quantification: A Distance-Based Approach ICML 2024 shapiq: Shapley Interactions for Machine Learning NeurIPS 2024 SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification AISTATS 2024 AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration AAAI 2023 A Survey of Methods for Automated Algorithm Configuration (Extended Abstract) IJCAI 2023 Is the volume of a credal set a good measure for epistemic uncertainty? UAI 2023 Koopman Kernel Regression NeurIPS 2023 Memorization-Dilation: Modeling Neural Collapse Under Noise ICLR 2023 On Second-Order Scoring Rules for Epistemic Uncertainty Quantification ICML 2023 On the Calibration of Probabilistic Classifier Sets AISTATS 2023 Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? UAI 2023 SHAP-IQ: Unified Approximation of any-order Shapley Interactions NeurIPS 2023 Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget NeurIPS 2022 Machine Learning for Online Algorithm Selection under Censored Feedback AAAI 2022 Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation NeurIPS 2022 Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison UAI 2022 Set-valued prediction in hierarchical classification with constrained representation complexity UAI 2022 Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models ICML 2022 Credal Self-Supervised Learning NeurIPS 2021 From Label Smoothing to Label Relaxation AAAI 2021 Identification of the Generalized Condorcet Winner in Multi-dueling Bandits NeurIPS 2021 Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model CVPR 2021 Preference-based Online Learning with Dueling Bandits: A Survey JMLR 2021 Robust Regression for Monocular Depth Estimation ACML 2021 Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model AAAI 2021 Testification of Condorcet Winners in dueling bandits UAI 2021 A Novel Higher-order Weisfeiler-Lehman Graph Convolution ACML 2020 Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals IJCAI 2020 Preselection Bandits ICML 2020 Reliable Multilabel Classification: Prediction with Partial Abstention AAAI 2020 Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis ACML 2020 Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA ACML 2019 Ranking Distributions based on Noisy Sorting ICML 2018 Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty IJCAI 2018 Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening ICML 2017 Extreme F-measure Maximization using Sparse Probability Estimates ICML 2016 Online F-Measure Optimization NeurIPS 2015 Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach NeurIPS 2015 Qualitative Multi-Armed Bandits: A Quantile-Based Approach ICML 2015 On the Bayes-Optimality of F-Measure Maximizers JMLR 2014 Learning to Rank Lexical Substitutions EMNLP 2013 Preference-Based CBR: General Ideas and Basic Principles IJCAI 2013 Label Ranking with Partial Abstention based on Thresholded Probabilistic Models NeurIPS 2012 An Exact Algorithm for F-Measure Maximization NeurIPS 2011