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ORCIDEyke Hüllermeier
Person information
- affiliation: LMU Munich, Germany
- affiliation (former): Paderborn University, Germany
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2020 – today
- 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) - [c226]Pritha Gupta, Jan Peter Drees, Eyke Hüllermeier:
Automated Side-Channel Attacks using Black-Box Neural Architecture Search. ARES 2023: 5:1-5:11 - [c225]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 - [c224]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On the Calibration of Probabilistic Classifier Sets. AISTATS 2023: 8857-8870 - [c223]Julian Lienen, Caglar Demir, Eyke Hüllermeier:
Conformal Credal Self-Supervised Learning. COPA 2023: 214-233 - [c222]Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier:
Conformal Prediction with Partially Labeled Data. COPA 2023: 251-266 - [c221]Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier:
Probabilistic Scoring Lists for Interpretable Machine Learning. DS 2023: 189-203 - [c220]Marcel Wever, Miran Özdogan, Eyke Hüllermeier:
Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. GECCO 2023: 597-605 - [c219]Duc Anh Nguyen, Ron Levie, Julian Lienen, Eyke Hüllermeier, Gitta Kutyniok:
Memorization-Dilation: Modeling Neural Collapse Under Noise. ICLR 2023 - [c218]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML 2023: 2078-2091 - [c217]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 - [c216]Anna-Katharina Wickert, Clemens Damke, Lars Baumgärtner, Eyke Hüllermeier, Mira Mezini:
UnGoML: Automated Classification of unsafe Usages in Go. MSR 2023: 309-321 - [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 - [i93]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR abs/2301.12736 (2023) - [i92]Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier:
Iterative Deepening Hyperband. CoRR abs/2302.00511 (2023) - [i91]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Approximating the Shapley Value without Marginal Contributions. CoRR abs/2302.00736 (2023) - [i90]Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. CoRR abs/2303.01179 (2023) - [i89]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) - [i88]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) - [i87]Svenja Uhlemeyer, Julian Lienen, Eyke Hüllermeier, Hanno Gottschalk:
Detecting Novelties with Empty Classes. CoRR abs/2305.00983 (2023) - [i86]Julian Lienen, Eyke Hüllermeier:
Mitigating Label Noise through Data Ambiguation. CoRR abs/2305.13764 (2023) - [i85]Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski, Eyke Hüllermeier, Sandra Hirche:
Koopman Kernel Regression. CoRR abs/2305.16215 (2023) - [i84]Anna-Katharina Wickert, Clemens Damke, Lars Baumgärtner, Eyke Hüllermeier, Mira Mezini:
UNGOML: Automated Classification of unsafe Usages in Go. CoRR abs/2306.00694 (2023) - [i83]Alireza Javanmardi, Yusuf Sale, Paul Hofman, Eyke Hüllermeier:
Conformal Prediction with Partially Labeled Data. CoRR abs/2306.01191 (2023) - [i82]Maximilian Muschalik, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer, Eyke Hüllermeier:
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios. CoRR abs/2306.07775 (2023) - [i81]Yusuf Sale, Michele Caprio, Eyke Hüllermeier:
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? CoRR abs/2306.09586 (2023) - [i80]Michele Caprio, Yusuf Sale, Eyke Hüllermeier, Insup Lee:
A Novel Bayes' Theorem for Upper Probabilities. CoRR abs/2307.06831 (2023) - [i79]Sascha Henzgen, Eyke Hüllermeier:
Weighting by Tying: A New Approach to Weighted Rank Correlation. CoRR abs/2308.10622 (2023) - [i78]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) - [i77]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) - [i76]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. CoRR abs/2310.00750 (2023) - [i75]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) - [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]Eyke Hüllermeier, Sébastien Destercke, Mohammad Hossein Shaker:
Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison. UAI 2022: 548-557 - [c196]Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. UAI 2022: 1392-1401 - [e12]Tassadit Bouadi, Élisa Fromont, Eyke Hüllermeier:
Advances in Intelligent Data Analysis XX - 20th International Symposium on Intelligent Data Analysis, IDA 2022, Rennes, France, April 20-22, 2022, Proceedings. Lecture Notes in Computer Science 13205, Springer 2022, ISBN 978-3-031-01332-4 [contents] - [i73]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Non-Stationary Dueling Bandits. CoRR abs/2202.00935 (2022) - [i72]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration. CoRR abs/2202.01651 (2022) - [i71]Jasmin Brandt, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. CoRR abs/2202.04487 (2022) - [i70]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. CoRR abs/2202.04593 (2022) - [i69]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. CoRR abs/2203.06102 (2022) - [i68]Thomas Mortier, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Set-valued prediction in hierarchical classification with constrained representation complexity. CoRR abs/2203.06676 (2022) - [i67]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On Calibration of Ensemble-Based Credal Predictors. CoRR abs/2205.10082 (2022) - [i66]Julian Lienen, Caglar Demir, Eyke Hüllermeier:
Conformal Credal Self-Supervised Learning. CoRR abs/2205.15239 (2022) - [i65]Duc Anh Nguyen, Ron Levie, Julian Lienen, Gitta Kutyniok, Eyke Hüllermeier:
Memorization-Dilation: Modeling Neural Collapse Under Noise. CoRR abs/2206.05530 (2022) - [i64]Mohamed Karim Belaid, Eyke Hüllermeier, Maximilian Rabus, Ralf Krestel:
Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark. CoRR abs/2207.14160 (2022) - [i63]Fabian Fumagalli, Maximilian Muschalik, Eyke Hüllermeier, Barbara Hammer:
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams. CoRR abs/2209.01939 (2022) - [i62]Eyke Hüllermeier:
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? CoRR abs/2209.03302 (2022) - [i61]Jasmin Brandt, Elias Schede, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier, Kevin Tierney:
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. CoRR abs/2212.00333 (2022) - [i60]Alireza Javanmardi, Eyke Hüllermeier:
Conformal Prediction Intervals for Remaining Useful Lifetime Estimation. CoRR abs/2212.14612 (2022) - 2021
- [j124]Daniel Weber, Stefan Heid, Henrik Bode, Jarren H. Lange, Eyke Hüllermeier, Oliver Wallscheid:
Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. IEEE Access 9: 35654-35669 (2021) - [j123]Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman:
Efficient set-valued prediction in multi-class classification. Data Min. Knowl. Discov. 35(4): 1435-1469 (2021) - [j122]Ammar Shaker, Eyke Hüllermeier:
TSK-Streams: learning TSK fuzzy systems for regression on data streams. Data Min. Knowl. Discov. 35(5): 1941-1971 (2021) - [j121]Julian Lienen, Eyke Hüllermeier:
Instance weighting through data imprecisiation. Int. J. Approx. Reason. 134: 1-14 (2021) - [j120]Andrea Campagner, Davide Ciucci, Eyke Hüllermeier:
Rough set-based feature selection for weakly labeled data. Int. J. Approx. Reason. 136: 150-167 (2021) - [j119]Vu-Linh Nguyen, Eyke Hüllermeier:
Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. J. Artif. Intell. Res. 72: 613-665 (2021) - [j118]Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier:
Preference-based Online Learning with Dueling Bandits: A Survey. J. Mach. Learn. Res. 22: 7:1-7:108 (2021) - [j117]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3): 457-506 (2021) - [j116]Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
On testing transitivity in online preference learning. Mach. Learn. 110(8): 2063-2084 (2021) - [j115]Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hüllermeier:
AutoML for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3037-3054 (2021) - [j114]Felix Mohr, Marcel Wever, Alexander Tornede, Eyke Hüllermeier:
Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. IEEE Trans. Pattern Anal. Mach. Intell. 43(9): 3055-3066 (2021) - [j113]Katharina J. Rohlfing, Philipp Cimiano, Ingrid Scharlau, Tobias Matzner, Heike M. Buhl, Hendrik Buschmeier, Elena Esposito, Angela Grimminger, Barbara Hammer, Reinhold Häb-Umbach, Ilona Horwath, Eyke Hüllermeier, Friederike Kern, Stefan Kopp, Kirsten Thommes, Axel-Cyrille Ngonga Ngomo, Carsten Schulte, Henning Wachsmuth, Petra Wagner, Britta Wrede:
Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Trans. Cogn. Dev. Syst. 13(3): 717-728 (2021) - [j112]Sadegh Abbaszadeh, Eyke Hüllermeier:
Machine Learning With the Sugeno Integral: The Case of Binary Classification. IEEE Trans. Fuzzy Syst. 29(12): 3723-3733 (2021) - [c195]Julian Lienen, Eyke Hüllermeier:
From Label Smoothing to Label Relaxation. AAAI 2021: 8583-8591 - [c194]Felix Mohr, Viktor Bengs, Eyke Hüllermeier:
Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI 2021: 12373-12381 - [c193]Julian Lienen, Nils Nommensen, Ralph Ewerth, Eyke Hüllermeier:
Robust Regression for Monocular Depth Estimation. ACML 2021: 1001-1016 - [c192]Jan Peter Drees, Pritha Gupta, Eyke Hüllermeier, Tibor Jager, Alexander Konze, Claudia Priesterjahn, Arunselvan Ramaswamy, Juraj Somorovsky:
Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! AISec@CCS 2021: 169-180 - [c191]Julian Lienen, Eyke Hüllermeier, Ralph Ewerth, Nils Nommensen:
Monocular Depth Estimation via Listwise Ranking Using the Plackett-Luce Model. CVPR 2021: 14595-14604 - [c190]Clemens Damke, Eyke Hüllermeier:
Ranking Structured Objects with Graph Neural Networks. DS 2021: 166-180 - [c189]Tanja Tornede, Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. GECCO 2021: 368-376 - [c188]Mohsen Ahmadi Fahandar, Eyke Hüllermeier:
Analogical Embedding for Analogy-Based Learning to Rank. IDA 2021: 76-88 - [c187]Sven Peeters, Vitalik Melnikov, Eyke Hüllermeier:
Performance Prediction for Hardware-Software Configurations: A Case Study for Video Games. IDA 2021: 222-234 - [c186]Robert Feldhans, Adrian Wilke, Stefan Heindorf, Mohammad Hossein Shaker, Barbara Hammer, Axel-Cyrille Ngonga Ngomo, Eyke Hüllermeier:
Drift Detection in Text Data with Document Embeddings. IDEAL 2021: 107-118 - [c185]Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag:
On the Identifiability of Hierarchical Decision Models. KR 2021: 151-161 - [c184]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Identifying Top-k Players in Cooperative Games via Shapley Bandits. LWDA 2021: 133-144 - [c183]Matthias Springstein, Stefanie Schneider, Javad Rahnama, Eyke Hüllermeier, Hubertus Kohle, Ralph Ewerth:
iART: A Search Engine for Art-Historical Images to Support Research in the Humanities. ACM Multimedia 2021: 2801-2803 - [c182]Julian Lienen, Eyke Hüllermeier:
Credal Self-Supervised Learning. NeurIPS 2021: 14370-14382 - [c181]Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. NeurIPS 2021: 25904-25916 - [c180]Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. PAKDD (1) 2021: 152-163 - [c179]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-Based Label Binning in Multi-label Classification. ECML/PKDD (3) 2021: 462-477 - [c178]Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier:
Testification of Condorcet Winners in dueling bandits. UAI 2021: 1195-1205 - [i59]Michael Dellnitz, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum, Christian Offen, Sebastian Peitz, Karlson Pfannschmidt:
Efficient time stepping for numerical integration using reinforcement learning. CoRR abs/2104.03562 (2021) - [i58]Clemens Damke, Eyke Hüllermeier:
Ranking Structured Objects with Graph Neural Networks. CoRR abs/2104.08869 (2021) - [i57]Marie-Luis Merten, Marcel Wever, Michaela Geierhos, Doris Tophinke, Eyke Hüllermeier:
Annotation Uncertainty in the Context of Grammatical Change. CoRR abs/2105.07270 (2021) - [i56]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-based Label Binning in Multi-label Classification. CoRR abs/2106.11690 (2021) - [i55]Julian Lienen, Eyke Hüllermeier:
Credal Self-Supervised Learning. CoRR abs/2106.11853 (2021) - [i54]Alexander Tornede, Lukas Gehring, Tanja Tornede, Marcel Wever, Eyke Hüllermeier:
Algorithm Selection on a Meta Level. CoRR abs/2107.09414 (2021) - [i53]Mohammad Hossein Shaker, Eyke Hüllermeier:
Ensemble-based Uncertainty Quantification: Bayesian versus Credal Inference. CoRR abs/2107.10384 (2021) - [i52]Matthias Springstein, Stefanie Schneider, Javad Rahnama, Eyke Hüllermeier, Hubertus Kohle, Ralph Ewerth:
iART: A Search Engine for Art-Historical Images to Support Research in the Humanities. CoRR abs/2108.01542 (2021) - [i51]Eyke Hüllermeier, Felix Mohr, Alexander Tornede, Marcel Wever:
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. CoRR abs/2109.04744 (2021) - [i50]Alexander Tornede, Viktor Bengs, Eyke Hüllermeier:
Machine Learning for Online Algorithm Selection under Censored Feedback. CoRR abs/2109.06234 (2021) - [i49]Tanja Tornede, Alexander Tornede, Jonas Hanselle, Marcel Wever, Felix Mohr, Eyke Hüllermeier:
Towards Green Automated Machine Learning: Status Quo and Future Directions. CoRR abs/2111.05850 (2021) - [i48]