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ORCIDEyke Hüllermeier
Person information
- affiliation: LMU Munich, Germany
- affiliation (former): Paderborn University, Germany
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2020 – today
- 2024
[j143]Eyke Hüllermeier
, Roman Slowinski:
Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies-part I. 4OR 22(2): 179-209 (2024)- [j142]Eyke Hüllermeier, Roman Slowinski:
Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies - part II. 4OR 22(3): 313-349 (2024) - [j141]Michael Rapp, Johannes Fürnkranz, Eyke Hüllermeier:
On the efficient implementation of classification rule learning. Adv. Data Anal. Classif. 18(4): 851-892 (2024) - [j140]Stefan Haas, Konstantin Hegestweiler, Michael Rapp, Maximilian Muschalik, Eyke Hüllermeier:
Stakeholder-centric explanations for black-box decisions: an XAI process model and its application to automotive goodwill assessments. Frontiers Artif. Intell. 7 (2024) - [j139]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: 109190 (2024) - [j138]Stefan Heid, Marcel Wever, Eyke Hüllermeier:
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. J. Data Min. Digit. Humanit. 2024 (2024) - [j137]Simone Camberg, Eyke Hüllermeier:
An Extensive Analysis of Different Approaches to Driver Gaze Classification. IEEE Trans. Intell. Transp. Syst. 25(11): 16435-16448 (2024) - [j136]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) - [c248]Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier:
Approximating the Shapley Value without Marginal Contributions. AAAI 2024: 13246-13255 - [c247]Julian Lienen, Eyke Hüllermeier:
Mitigating Label Noise through Data Ambiguation. AAAI 2024: 13799-13807 - [c246]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. AAAI 2024: 14388-14396 - [c245]Timo Löhr, Michael Ingrisch, Eyke Hüllermeier:
Towards Aleatoric and Epistemic Uncertainty in Medical Image Classification. AIME (2) 2024: 145-155 - [c244]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. AISTATS 2024: 226-234 - [c243]Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. AISTATS 2024: 3520-3528 - [c242]Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee:
A Novel Bayes' Theorem for Upper Probabilities. Epi UAI 2024: 1-12 - [c241]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 - [c240]Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. ICML 2024 - [c239]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. ICML 2024 - [c238]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? ICML 2024 - [c237]Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier:
Second-Order Uncertainty Quantification: A Distance-Based Approach. ICML 2024 - [c236]Jasmin Brandt, Marcel Wever, Viktor Bengs, Eyke Hüllermeier:
Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. IJCAI 2024: 3742-3750 - [c235]Amihossein Vahidi, Lisa Wimmer, Hüseyin Anil Gündüz, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei:
Diversified Ensemble of Independent Sub-networks for Robust Self-supervised Representation Learning. ECML/PKDD (1) 2024: 38-55 - [c234]Clemens Damke, Eyke Hüllermeier:
CUQ-GNN: Committee-Based Graph Uncertainty Quantification Using Posterior Networks. ECML/PKDD (8) 2024: 306-323 - [c233]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
Explaining Change in Models and Data with Global Feature Importance and Effects. TempXAI@PKDD/ECML 2024: 1-6 - [c232]Patrick Kolpaczki, Georg Haselbeck, Eyke Hüllermeier:
How Much Can Stratification Improve the Approximation of Shapley Values? xAI (2) 2024: 489-512 - [e14]Jose M. Alonso-Moral, Zach Anthis, Rafael Berlanga, Alejandro Catalá, Philipp Cimiano, Peter Flach, Eyke Hüllermeier, Tim Miller, Oana Mitrut, Dimitry Mindlin, Gabriela Moise, Alin Moldoveanu, Florica Moldoveanu, Kacper Sokol, Aitor Soroa:
Proceedings of the First Multimodal, Affective and Interactive eXplainable AI Workshop (MAI-XAI24 2024) co-located with 27th European Conference On Artificial Intelligence 19-24 October 2024 (ECAI 2024), Santiago de Compostela, Spain, October 19, 2024. CEUR Workshop Proceedings 3803, CEUR-WS.org 2024 [contents] - [e13]Zahraa S. Abdallah, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier, Matthias Jakobs, Emmanuel Müller, Maximilian Muschalik, Panagiotis Papapetrou, Amal Saadallah, George Tzagkarakis:
Proceedings of the Workshop on Explainable AI for Time Series and Data Streams (TempXAI 2024) co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2024), Vilnius, Lithuania, September 9th, 2024. CEUR Workshop Proceedings 3761, CEUR-WS.org 2024 [contents] - [i115]Yusuf Sale, Paul Hofman, Lisa Wimmer, Eyke Hüllermeier, Thomas Nagler:
Second-Order Uncertainty Quantification: Variance-Based Measures. CoRR abs/2401.00276 (2024) - [i114]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles. CoRR abs/2401.12069 (2024) - [i113]Patrick Kolpaczki, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification. CoRR abs/2401.13371 (2024) - [i112]Pritha Gupta, Marcel Wever, Eyke Hüllermeier:
Information Leakage Detection through Approximate Bayes-optimal Prediction. CoRR abs/2401.14283 (2024) - [i111]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR abs/2402.09056 (2024) - [i110]Alireza Javanmardi, David Stutz, Eyke Hüllermeier:
Conformalized Credal Set Predictors. CoRR abs/2402.10723 (2024) - [i109]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 abs/2403.04629 (2024) - [i108]Paul Hofman, Yusuf Sale, Eyke Hüllermeier:
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules. CoRR abs/2404.12215 (2024) - [i107]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 abs/2405.02200 (2024) - [i106]Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions. CoRR abs/2405.10852 (2024) - [i105]Yusuf Sale, Paul Hofman, Timo Löhr, Lisa Wimmer, Thomas Nagler, Eyke Hüllermeier:
Label-wise Aleatoric and Epistemic Uncertainty Quantification. CoRR abs/2406.02354 (2024) - [i104]Clemens Damke, Eyke Hüllermeier:
Linear Opinion Pooling for Uncertainty Quantification on Graphs. CoRR abs/2406.04041 (2024) - [i103]Arduin Findeis, Timo Kaufmann, Eyke Hüllermeier, Samuel Albanie, Robert Mullins:
Inverse Constitutional AI: Compressing Preferences into Principles. CoRR abs/2406.06560 (2024) - [i102]Timo Kaufmann, Jannis Blüml, Antonia Wüst, Quentin Delfosse, Kristian Kersting, Eyke Hüllermeier:
OCALM: Object-Centric Assessment with Language Models. CoRR abs/2406.16748 (2024) - [i101]Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier:
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. CoRR abs/2406.17322 (2024) - [i100]Mohamed Karim Belaid, Maximilian Rabus, Eyke Hüllermeier:
Pairwise Difference Learning for Classification. CoRR abs/2406.20031 (2024) - [i99]Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier:
Probabilistic Scoring Lists for Interpretable Machine Learning. CoRR abs/2407.21535 (2024) - [i98]Subhabrata Dutta, Timo Kaufmann, Goran Glavas, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, Hinrich Schütze:
Problem Solving Through Human-AI Preference-Based Cooperation. CoRR abs/2408.07461 (2024) - [i97]Clemens Damke, Eyke Hüllermeier:
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks. CoRR abs/2409.04159 (2024) - [i96]Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, Eyke Hüllermeier:
shapiq: Shapley Interactions for Machine Learning. CoRR abs/2410.01649 (2024) - 2023
- [j135]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: 427-457 (2023) - [j134]Viktor Bengs, Eyke Hüllermeier:
Multi-armed bandits with censored consumption of resources. Mach. Learn. 112(1): 217-240 (2023) - [j133]Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier:
Algorithm selection on a meta level. Mach. Learn. 112(4): 1253-1286 (2023) - [j132]Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer:
Incremental permutation feature importance (iPFI): towards online explanations on data streams. Mach. Learn. 112(12): 4863-4903 (2023) - [j131]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): 579- (2023) - [c231]Pritha Gupta, Jan Peter Drees, Eyke Hüllermeier:
Automated Side-Channel Attacks using Black-Box Neural Architecture Search. ARES 2023: 5:1-5:11 - [c230]Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI 2023: 12355-12363 - [c229]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On the Calibration of Probabilistic Classifier Sets. AISTATS 2023: 8857-8870 - [c228]Julian Lienen, Caglar Demir, Eyke Hüllermeier:
Conformal Credal Self-Supervised Learning. COPA 2023: 214-233 - [c227]Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier:
Conformal Prediction with Partially Labeled Data. COPA 2023: 251-266 - [c226]Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier:
Probabilistic Scoring Lists for Interpretable Machine Learning. DS 2023: 189-203 - [c225]Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer:
On Feature Removal for Explainability in Dynamic Environments. ESANN 2023 - [c224]Marcel Wever, Miran Özdogan, Eyke Hüllermeier:
Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. GECCO 2023: 597-605 - [c223]Duc Anh Nguyen, Ron Levie, Julian Lienen, Eyke Hüllermeier, Gitta Kutyniok:
Memorization-Dilation: Modeling Neural Collapse Under Noise. ICLR 2023 - [c222]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML 2023: 2078-2091 - [c221]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: 6964-6968 - [c220]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: 430-441 - [c219]Jasmin Brandt, Elias Schede, Shivam Sharma, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. LWDA 2023: 492-505 - [c218]Anna-Katharina Wickert, Clemens Damke, Lars Baumgärtner, Eyke Hüllermeier, Mira Mezini:
UnGoML: Automated Classification of unsafe Usages in Go. MSR 2023: 309-321 - [c217]Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche:
Koopman Kernel Regression. NeurIPS 2023 - [c216]Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. NeurIPS 2023 - [c215]Stefan Haas, Eyke Hüllermeier:
Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing. ECML/PKDD (6) 2023: 3-18 - [c214]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. ECML/PKDD (3) 2023: 428-445 - [c213]Arnab Sharma, Vitalik Melnikov, Eyke Hüllermeier, Heike Wehrheim:
Property-Driven Black-Box Testing of Numeric Functions. Software Engineering 2023: 111-112 - [c212]Yusuf Sale, Michele Caprio, Eyke Hüllermeier:
Is the volume of a credal set a good measure for epistemic uncertainty? UAI 2023: 1795-1804 - [c211]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: 2282-2292 - [c210]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: 88-109 - [c209]Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier:
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. xAI (1) 2023: 177-194 - [i95]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR abs/2301.12736 (2023) - [i94]Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier:
Iterative Deepening Hyperband. CoRR abs/2302.00511 (2023) - [i93]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Approximating the Shapley Value without Marginal Contributions. CoRR abs/2302.00736 (2023) - [i92]Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. CoRR abs/2303.01179 (2023) - [i91]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. CoRR abs/2303.01181 (2023) - [i90]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 abs/2304.01224 (2023) - [i89]Svenja Uhlemeyer, Julian Lienen, Eyke Hüllermeier, Hanno Gottschalk:
Detecting Novelties with Empty Classes. CoRR abs/2305.00983 (2023) - [i88]Julian Lienen, Eyke Hüllermeier:
Mitigating Label Noise through Data Ambiguation. CoRR abs/2305.13764 (2023) - [i87]Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche:
Koopman Kernel Regression. CoRR abs/2305.16215 (2023) - [i86]Anna-Katharina Wickert, Clemens Damke, Lars Baumgärtner, Eyke Hüllermeier, Mira Mezini:
UNGOML: Automated Classification of unsafe Usages in Go. CoRR abs/2306.00694 (2023) - [i85]Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier:
Conformal Prediction with Partially Labeled Data. CoRR abs/2306.01191 (2023) - [i84]Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier:
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. CoRR abs/2306.07775 (2023) - [i83]Yusuf Sale, Michele Caprio, Eyke Hüllermeier:
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? CoRR abs/2306.09586 (2023) - [i82]Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee:
A Novel Bayes' Theorem for Upper Probabilities. CoRR abs/2307.06831 (2023) - [i81]Sascha Henzgen, Eyke Hüllermeier:
Weighting by Tying: A New Approach to Weighted Rank Correlation. CoRR abs/2308.10622 (2023) - [i80]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 abs/2308.14705 (2023) - [i79]Amirhossein Vahidi, Simon Schoßer, Lisa Wimmer, Yawei Li, Bernd Bischl, Eyke Hüllermeier, Mina Rezaei:
Probabilistic Self-supervised Learning via Scoring Rules Minimization. CoRR abs/2309.02048 (2023) - [i78]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. CoRR abs/2310.00750 (2023) - [i77]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. CoRR abs/2311.11908 (2023) - [i76]Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier:
Second-Order Uncertainty Quantification: A Distance-Based Approach. CoRR abs/2312.00995 (2023) - [i75]Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier:
A Survey of Reinforcement Learning from Human Feedback. CoRR abs/2312.14925 (2023) - [i74]Pritha Gupta, Jan Peter Drees, Eyke Hüllermeier:
Automated Side-Channel Attacks using Black-Box Neural Architecture Search. IACR Cryptol. ePrint Arch. 2023: 93 (2023) - 2022
- [j130]Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier:
Learning context-dependent choice functions. Int. J. Approx. Reason. 140: 116-155 (2022) - [j129]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration. J. Artif. Intell. Res. 75: 425-487 (2022) - [j128]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
Agnostic Explanation of Model Change based on Feature Importance. Künstliche Intell. 36(3): 211-224 (2022) - [j127]Vu-Linh Nguyen, Mohammad Hossein Shaker, Eyke Hüllermeier:
How to measure uncertainty in uncertainty sampling for active learning. Mach. Learn. 111(1): 89-122 (2022) - [j126]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): 713-737 (2022) - [j125]Arunselvan Ramaswamy, Eyke Hüllermeier:
Deep Q-Learning: Theoretical Insights From an Asymptotic Analysis. IEEE Trans. Artif. Intell. 3(2): 139-151 (2022) - [c208]Alexander Tornede, Viktor Bengs, Eyke Hüllermeier:
Machine Learning for Online Algorithm Selection under Censored Feedback. AAAI 2022: 10370-10380 - [c207]Stefanie Schneider, Matthias Springstein, Javad Rahnama, Hubertus Kohle, Ralph Ewerth, Eyke Hüllermeier:
iART - Eine Suchmaschine zur Unterstützung von bildorientierten Forschungsprozessen. DHd 2022 - [c206]Pritha Gupta, Arunselvan Ramaswamy, Jan Peter Drees, Eyke Hüllermeier, Claudia Priesterjahn, Tibor Jager:
Automated Information Leakage Detection: A New Method Combining Machine Learning and Hypothesis Testing with an Application to Side-channel Detection in Cryptographic Protocols. ICAART (2) 2022: 152-163 - [c205]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. ICML 2022: 1764-1786 - [c204]Arnab Sharma, Vitalik Melnikov, Eyke Hüllermeier, Heike Wehrheim:
Property-Driven Testing of Black-Box Functions. FormaliSE@ICSE 2022: 113-123 - [c203]Eyke Hüllermeier:
Representation and quantification of uncertainty in machine learning. LFA 2022 - [c202]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS 2022 - [c201]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 - [c200]Stefan Haas, Eyke Hüllermeier:
A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain. ECML/PKDD (6) 2022: 170-184 - [c199]Andrea Campagner, Julian Lienen, Eyke Hüllermeier, Davide Ciucci:
Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. IJCRS 2022: 57-70 - [c198]Julian Rodemann, Dominik Kreiss, Eyke Hüllermeier, Thomas Augustin:
Levelwise Data Disambiguation by Cautious Superset Classification. SUM 2022: 263-276 - [c197]