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ORCIDEyke Hüllermeier
Person information
- affiliation: LMU Munich, Germany
- affiliation (former): Paderborn University, Germany
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2020 – today
- 2023
[j130]Viktor Bengs
, Eyke Hüllermeier:
Multi-armed bandits with censored consumption of resources. Mach. Learn. 112(1): 217-240 (2023)- [c206]Arnab Sharma, Vitalik Melnikov, Eyke Hüllermeier, Heike Wehrheim:
Property-Driven Black-Box Testing of Numeric Functions. Software Engineering 2023: 111-112 - [i79]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR abs/2301.12736 (2023) - [i78]Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier:
Iterative Deepening Hyperband. CoRR abs/2302.00511 (2023) - [i77]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Approximating the Shapley Value without Marginal Contributions. CoRR abs/2302.00736 (2023) - [i76]Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki, Eyke Hüllermeier, Barbara Hammer:
SHAP-IQ: Unified Approximation of any-order Shapley Interactions. CoRR abs/2303.01179 (2023) - [i75]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) - [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
- [j129]Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier:
Learning context-dependent choice functions. Int. J. Approx. Reason. 140: 116-155 (2022) - [j128]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) - [j127]Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer, Eyke Hüllermeier:
Agnostic Explanation of Model Change based on Feature Importance. Künstliche Intell. 36(3): 211-224 (2022) - [j126]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) - [j125]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) - [j124]Arunselvan Ramaswamy, Eyke Hüllermeier:
Deep Q-Learning: Theoretical Insights From an Asymptotic Analysis. IEEE Trans. Artif. Intell. 3(2): 139-151 (2022) - [c205]Alexander Tornede, Viktor Bengs, Eyke Hüllermeier:
Machine Learning for Online Algorithm Selection under Censored Feedback. AAAI 2022: 10370-10380 - [c204]Stefanie Schneider, Matthias Springstein, Javad Rahnama, Hubertus Kohle, Ralph Ewerth, Eyke Hüllermeier:
iART - Eine Suchmaschine zur Unterstützung von bildorientierten Forschungsprozessen. DHd 2022 - [c203]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 - [c202]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. ICML 2022: 1764-1786 - [c201]Arnab Sharma, Vitalik Melnikov, Eyke Hüllermeier, Heike Wehrheim:
Property-Driven Testing of Black-Box Functions. FormaliSE@ICSE 2022: 113-123 - [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
- [j123]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) - [j122]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) - [j121]Ammar Shaker, Eyke Hüllermeier:
TSK-Streams: learning TSK fuzzy systems for regression on data streams. Data Min. Knowl. Discov. 35(5): 1941-1971 (2021) - [j120]Julian Lienen, Eyke Hüllermeier:
Instance weighting through data imprecisiation. Int. J. Approx. Reason. 134: 1-14 (2021) - [j119]Andrea Campagner, Davide Ciucci, Eyke Hüllermeier:
Rough set-based feature selection for weakly labeled data. Int. J. Approx. Reason. 136: 150-167 (2021) - [j118]Vu-Linh Nguyen, Eyke Hüllermeier:
Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. J. Artif. Intell. Res. 72: 613-665 (2021) - [j117]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) - [j116]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Mach. Learn. 110(3): 457-506 (2021) - [j115]Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
On testing transitivity in online preference learning. Mach. Learn. 110(8): 2063-2084 (2021) - [j114]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) - [j113]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) - [j112]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) - [j111]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]Eyke Hüllermeier:
Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities. CoRR abs/2112.08268 (2021) - [i47]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! IACR Cryptol. ePrint Arch. 2021: 591 (2021) - 2020
- [j110]Cedric Richter, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim:
Algorithm selection for software validation based on graph kernels. Autom. Softw. Eng. 27(1): 153-186 (2020) - [j109]Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier:
Introduction to the special issue of the ECML PKDD 2020 journal track. Data Min. Knowl. Discov. 34(5): 1235-1236 (2020) - [j108]Björn Haddenhorst, Eyke Hüllermeier, Martin Kolb:
Generalized transitivity: A systematic comparison of concepts with an application to preferences in the Babington Smith model. Int. J. Approx. Reason. 119: 373-407 (2020) - [d1]Stefan Heid, Daniel Weber, Henrik Bode, Eyke Hüllermeier, Oliver Wallscheid:
OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. J. Open Source Softw. 5(54): 2435 (2020) - [j107]Ira Assent, Carlotta Domeniconi, Aristides Gionis, Eyke Hüllermeier:
Introduction to the special issue of the ECML PKDD 2020 journal track. Mach. Learn. 109(9-10): 1697-1698 (2020) - [j106]Katharina J. Rohlfing, Giuseppe Leonardi, Iris Nomikou, Joanna Raczaszek-Leonardi, Eyke Hüllermeier:
Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Trans. Cogn. Dev. Syst. 12(2): 260-271 (2020) - [c177]Vu-Linh Nguyen, Eyke Hüllermeier:
Reliable Multilabel Classification: Prediction with Partial Abstention. AAAI 2020: 5264-5271 - [c176]Clemens Damke, Vitalik Melnikov, Eyke Hüllermeier:
A Novel Higher-order Weisfeiler-Lehman Graph Convolution. ACML 2020: 49-64 - [c175]Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier:
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. ACML 2020: 737-752 - [c174]Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Extreme Algorithm Selection with Dyadic Feature Representation. DS 2020: 309-324 - [c173]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. DS 2020: 533-547 - [c172]Stefanie Schneider, Matthias Springstein, Javad Rahnama, Eyke Hüllermeier, Ralph Ewerth, Hubertus Kohle:
The Dissimilar in the Similar. An Attribute-guided Approach to the Subject-specific Classification of Art-historical Objects. GI-Jahrestagung 2020: 1355-1364 - [c171]Viktor Bengs, Eyke Hüllermeier:
Preselection Bandits. ICML 2020: 778-787 - [c170]Mohammad Hossein Shaker, Eyke Hüllermeier:
Aleatoric and Epistemic Uncertainty with Random Forests. IDA 2020: 444-456 - [c169]Marcel Wever, Alexander Tornede, Felix Mohr, Eyke Hüllermeier:
LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. IDA 2020: 561-573 - [c168]Roman Bresson, Johanne Cohen, Eyke Hüllermeier, Christophe Labreuche, Michèle Sebag:
Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. IJCAI 2020: 1984-1991 - [c167]Simone Dari, Nikolay Kadrileev, Eyke Hüllermeier:
A Neural Network-Based Driver Gaze Classification System with Vehicle Signals. IJCNN 2020: 1-7 - [c166]Javad Rahnama, Eyke Hüllermeier:
Learning Tversky Similarity. IPMU (2) 2020: 269-280 - [c165]Andrea Campagner, Davide Ciucci, Eyke Hüllermeier:
Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. IPMU (1) 2020: 471-484 - [c164]Jonas Hanselle, Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Hybrid Ranking and Regression for Algorithm Selection. KI 2020: 59-72 - [c163]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. KI 2020: 290-296 - [c162]Karlson Pfannschmidt, Eyke Hüllermeier:
Learning Choice Functions via Pareto-Embeddings. KI 2020: 327-333 - [c161]Adil El Mesaoudi-Paul, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. LION 2020: 216-232 - [c160]Eyke Hüllermeier:
Towards Analogy-Based Explanations in Machine Learning. MDAI 2020: 205-217 - [c159]Eyke Hüllermeier:
How to Measure Uncertainty in Uncertainty Sampling for Active Learning. IAL@PKDD/ECML 2020: 3 - [c158]Tanja Tornede, Alexander Tornede, Marcel Wever, Felix Mohr, Eyke Hüllermeier:
AutoML for Predictive Maintenance: One Tool to RUL Them All. IoT Streams/ITEM@PKDD/ECML 2020: 106-118 - [c157]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. ECML/PKDD (3) 2020: 124-140 - [c156]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, Michael Rapp:
Rule-Based Multi-label Classification: Challenges and Opportunities. RuleML+RR 2020: 3-19 - [i46]Mohammad Hossein Shaker, Eyke Hüllermeier:
Aleatoric and Epistemic Uncertainty with Random Forests. CoRR abs/2001.00893 (2020) - [i45]Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Extreme Algorithm Selection With Dyadic Feature Representation. CoRR abs/2001.10741 (2020) - [i44]Adil El Mesaoudi-Paul, Viktor Bengs, Eyke Hüllermeier:
Online Preselection with Context Information under the Plackett-Luce Model. CoRR abs/2002.04275 (2020) - [i43]Henrik Bode, Stefan Heid, Daniel Weber, Eyke Hüllermeier, Oliver Wallscheid:
Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. CoRR abs/2005.04869 (2020) - [i42]Eyke Hüllermeier:
Towards Analogy-Based Explanations in Machine Learning. CoRR abs/2005.12800 (2020) - [i41]Javad Rahnama, Eyke Hüllermeier:
Learning Tversky Similarity. CoRR abs/2006.11372 (2020) - [i40]