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ORCIDEyke Hüllermeier
Person information
- affiliation: LMU Munich, Germany
- affiliation (former): Paderborn University, Germany
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Books and Theses
- 2007
[b2]Eyke Hüllermeier:
Case-Based Approximate Reasoning. Theory and Decision Library 44, Springer 2007, ISBN 978-1-4020-5694-9- 1997
- [b1]Eyke Hüllermeier:
Reasoning about systems based on incomplete and uncertain models. University of Paderborn, Germany, HNI-Verlagsschriftenreihe 20, 1997, ISBN 3-931466-19-1, pp. 1-195
Journal Articles
- 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) - 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) - 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) - 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) - 2020
- [j111]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) - [j110]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) - [j109]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) - [j108]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) - 2019
- [j105]Inés Couso, Christian Borgelt, Eyke Hüllermeier, Rudolf Kruse:
Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Comput. Intell. Mag. 14(1): 31-44 (2019) - [j104]Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier:
Multi-target prediction: a unifying view on problems and methods. Data Min. Knowl. Discov. 33(2): 293-324 (2019) - [j103]Vidal Kamdem Tagne, Siméon Fotso, Louis Aimé Fono, Eyke Hüllermeier:
Choice Functions Generated by Mallows and Plackett-Luce Relations. New Math. Nat. Comput. 15(2): 191-213 (2019) - [j102]Sascha Henzgen, Eyke Hüllermeier:
Mining Rank Data. ACM Trans. Knowl. Discov. Data 13(6): 59:1-59:36 (2019) - 2018
- [j101]Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, Ke Yi:
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). Dagstuhl Manifestos 7(1): 1-29 (2018) - [j100]Dirk Schäfer, Eyke Hüllermeier:
Dyad ranking using Plackett-Luce models based on joint feature representations. Mach. Learn. 107(5): 903-941 (2018) - [j99]Felix Mohr, Marcel Wever, Eyke Hüllermeier:
ML-Plan: Automated machine learning via hierarchical planning. Mach. Learn. 107(8-10): 1495-1515 (2018) - [j98]Vitalik Melnikov, Eyke Hüllermeier:
On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Mach. Learn. 107(8-10): 1537-1560 (2018) - 2017
- [j97]Michael Bräuning, Eyke Hüllermeier, Tobias Keller, Martin Glaum:
Lexicographic preferences for predictive modeling of human decision making: A new machine learning method with an application in accounting. Eur. J. Oper. Res. 258(1): 295-306 (2017) - [j96]Marie Christin Platenius, Ammar Shaker, Matthias Becker, Eyke Hüllermeier, Wilhelm Schäfer:
Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. IEEE Trans. Software Eng. 43(8): 739-759 (2017) - 2016
- [j95]Mona Riemenschneider, Robin Senge, Ursula Neumann, Eyke Hüllermeier, Dominik Heider:
Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. BioData Min. 9: 10 (2016) - [j94]Johannes Fürnkranz, Eyke Hüllermeier:
Special Issue on Discovery Science. Inf. Sci. 329: 849-850 (2016) - [j93]Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, Ke Yi:
Research Directions for Principles of Data Management (Abridged). SIGMOD Rec. 45(4): 5-17 (2016) - [j92]Matthias Leinweber, Thomas Fober, Marc Strickert, Lars Baumgärtner, Gerhard Klebe, Bernd Freisleben, Eyke Hüllermeier:
CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites. IEEE Trans. Knowl. Data Eng. 28(6): 1423-1434 (2016) - 2015
- [j91]Bernard De Baets, Didier Dubois, Eyke Hüllermeier:
Editorial. Fuzzy Sets Syst. 281: 1-3 (2015) - [j90]Eyke Hüllermeier:
Does machine learning need fuzzy logic? Fuzzy Sets Syst. 281: 292-299 (2015) - [j89]Ammar Shaker, Eyke Hüllermeier:
Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. Neurocomputing 150: 250-264 (2015) - [j88]Eyke Hüllermeier:
From knowledge-based to data-driven fuzzy modeling - Development, criticism, and alternative directions. Inform. Spektrum 38(6): 500-509 (2015) - [j87]Santiago García-Jiménez, Humberto Bustince, Eyke Hüllermeier, Radko Mesiar, Nikhil R. Pal, Ana Pradera:
Overlap Indices: Construction of and Application to Interpolative Fuzzy Systems. IEEE Trans. Fuzzy Syst. 23(4): 1259-1273 (2015) - [j86]Robin Senge, Eyke Hüllermeier:
Fast Fuzzy Pattern Tree Learning for Classification. IEEE Trans. Fuzzy Syst. 23(6): 2024-2033 (2015) - 2014
- [j85]Ammar Shaker, Eyke Hüllermeier:
Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. Int. J. Appl. Math. Comput. Sci. 24(1): 199-212 (2014) - [j84]Frank Hoffmann, Eyke Hüllermeier, Andreas Kroll:
Ausgewählte Beiträge des GMA-Fachausschusses 5.14 "Computational Intelligence". Autom. 62(10): 685-686 (2014) - [j83]Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo:
Guest editors' introduction: special issue of the ECML/PKDD 2014 journal track. Data Min. Knowl. Discov. 28(5-6): 1129-1133 (2014) - [j82]Sascha Henzgen, Marc Strickert, Eyke Hüllermeier:
Visualization of evolving fuzzy rule-based systems. Evol. Syst. 5(3): 175-191 (2014) - [j81]Eyke Hüllermeier:
Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. Int. J. Approx. Reason. 55(7): 1519-1534 (2014) - [j80]Eyke Hüllermeier:
Rejoinder on "Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization". Int. J. Approx. Reason. 55(7): 1609-1613 (2014) - [j79]Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier:
Correlation-based embedding of pairwise score data. Neurocomputing 141: 97-109 (2014) - [j78]Robin Senge, Stefan Bösner, Krzysztof Dembczynski, Jörg Haasenritter, Oliver Hirsch, Norbert Donner-Banzhoff, Eyke Hüllermeier:
Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. Inf. Sci. 255: 16-29 (2014) - [j77]Marco Mernberger, Daniel Moog, Simone Stork, Stefan Zauner, Uwe G. Maier, Eyke Hüllermeier:
Protein Sub-Cellular Localization Prediction for Special compartments via Optimized Time Series Distances. J. Bioinform. Comput. Biol. 12(1) (2014) - [j76]Willem Waegeman, Krzysztof Dembczynski, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier:
On the bayes-optimality of F-measure maximizers. J. Mach. Learn. Res. 15(1): 3333-3388 (2014) - [j75]Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo:
Guest Editors' introduction: special issue of the ECML/PKDD 2014 journal track. Mach. Learn. 97(1-2): 1-3 (2014) - [j74]Róbert Busa-Fekete, Balázs Szörényi, Paul Weng, Weiwei Cheng, Eyke Hüllermeier:
Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. Mach. Learn. 97(3): 327-351 (2014) - [j73]Elena Montañés, Robin Senge, José Barranquero, José Ramón Quevedo, Juan José del Coz, Eyke Hüllermeier:
Dependent binary relevance models for multi-label classification. Pattern Recognit. 47(3): 1494-1508 (2014) - [j72]Georg Krempl, Indre Zliobaite, Dariusz Brzezinski, Eyke Hüllermeier, Mark Last, Vincent Lemaire, Tino Noack, Ammar Shaker, Sonja Sievi, Myra Spiliopoulou, Jerzy Stefanowski:
Open challenges for data stream mining research. SIGKDD Explor. 16(1): 1-10 (2014) - [j71]Timo Krotzky, Thomas Fober, Eyke Hüllermeier, Gerhard Klebe:
Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. IEEE ACM Trans. Comput. Biol. Bioinform. 11(5): 878-890 (2014) - [j70]Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Identification of Functionally Related Enzymes by Learning-to-Rank Methods. IEEE ACM Trans. Comput. Biol. Bioinform. 11(6): 1157-1169 (2014) - 2013
- [j69]Dominik Heider, Robin Senge, Weiwei Cheng, Eyke Hüllermeier:
Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction. Bioinform. 29(16): 1946-1952 (2013) - [j68]Ammar Shaker, Robin Senge, Eyke Hüllermeier:
Evolving fuzzy pattern trees for binary classification on data streams. Inf. Sci. 220: 34-45 (2013) - [j67]Timo Krotzky, Thomas Fober, Marco Mernberger, Gerhard Klebe, Eyke Hüllermeier:
Extended graph-based models for enhanced similarity retrieval in Cavbase. J. Cheminformatics 5(S-1): 29 (2013) - [j66]Eyke Hüllermeier, Johannes Fürnkranz:
Editorial: Preference learning and ranking. Mach. Learn. 93(2-3): 185-189 (2013) - [j65]Thomas Fober, Marco Mernberger, Gerhard Klebe, Eyke Hüllermeier:
Graph-based methods for protein structure comparison. WIREs Data Mining Knowl. Discov. 3(5): 307-320 (2013) - 2012
- [j64]Frank Hoffmann, Eyke Hüllermeier, Andreas Kroll:
Ausgewählte Beiträge des GMA-Fachausschusses 5.14 "Computational Intelligence". Autom. 60(10): 587-588 (2012) - [j63]Robin Senge, Thomas Fober, Maryam Nasiri, Eyke Hüllermeier:
Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. Autom. 60(10): 622-629 (2012) - [j62]Ammar Shaker, Eyke Hüllermeier:
IBLStreams: a system for instance-based classification and regression on data streams. Evol. Syst. 3(4): 235-249 (2012) - [j61]M. Dolores Ruiz, Eyke Hüllermeier:
A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. Inf. Sci. 206: 1-17 (2012) - [j60]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
On label dependence and loss minimization in multi-label classification. Mach. Learn. 88(1-2): 5-45 (2012) - [j59]Johannes Fürnkranz, Eyke Hüllermeier, Weiwei Cheng, Sang-Hyeun Park:
Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Mach. Learn. 89(1-2): 123-156 (2012) - [j58]Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Learning monotone nonlinear models using the Choquet integral. Mach. Learn. 89(1-2): 183-211 (2012) - [j57]Martin Pyka, Alexander Balz, Andreas Jansen, Axel Krug, Eyke Hüllermeier:
A WEKA Interface for fMRI Data. Neuroinformatics 10(4): 409-413 (2012) - [j56]Humberto Bustince Sola, Miguel Pagola, Radko Mesiar, Eyke Hüllermeier, Francisco Herrera:
Grouping, Overlap, and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons. IEEE Trans. Fuzzy Syst. 20(3): 405-415 (2012) - [j55]Eyke Hüllermeier, Maria Rifqi, Sascha Henzgen, Robin Senge:
Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures. IEEE Trans. Fuzzy Syst. 20(3): 546-556 (2012) - [j54]Ali Fallah Tehrani, Weiwei Cheng, Eyke Hüllermeier:
Preference Learning Using the Choquet Integral: The Case of Multipartite Ranking. IEEE Trans. Fuzzy Syst. 20(6): 1102-1113 (2012) - 2011
- [j53]Carmel Domshlak, Eyke Hüllermeier, Souhila Kaci, Henri Prade:
Preferences in AI: An overview. Artif. Intell. 175(7-8): 1037-1052 (2011) - [j52]Eyke Hüllermeier:
Fuzzy sets in machine learning and data mining. Appl. Soft Comput. 11(2): 1493-1505 (2011) - [j51]Marco Mernberger, Gerhard Klebe, Eyke Hüllermeier:
SEGA: Semiglobal Graph Alignment for Structure-Based Protein Comparison. IEEE ACM Trans. Comput. Biol. Bioinform. 8(5): 1330-1343 (2011) - [j50]Thomas Fober, Serghei Glinca, Gerhard Klebe, Eyke Hüllermeier:
Superposition and Alignment of Labeled Point Clouds. IEEE ACM Trans. Comput. Biol. Bioinform. 8(6): 1653-1666 (2011) - [j49]Robin Senge, Eyke Hüllermeier:
Top-Down Induction of Fuzzy Pattern Trees. IEEE Trans. Fuzzy Syst. 19(2): 241-252 (2011) - [j48]Eyke Hüllermeier:
Fuzzy machine learning and data mining. WIREs Data Mining Knowl. Discov. 1(4): 269-283 (2011) - 2010
- [j47]Patrick Pfeffer, Thomas Fober, Eyke Hüllermeier, Gerhard Klebe:
GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm. J. Chem. Inf. Model. 50(9): 1644-1659 (2010) - [j46]Eyke Hüllermeier, Johannes Fürnkranz:
On predictive accuracy and risk minimization in pairwise label ranking. J. Comput. Syst. Sci. 76(1): 49-62 (2010) - [j45]Eyke Hüllermeier, Stijn Vanderlooy:
Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognit. 43(1): 128-142 (2010) - 2009
- [j44]Thomas Fober, Marco Mernberger, Gerhard Klebe, Eyke Hüllermeier:
Evolutionary construction of multiple graph alignments for the structural analysis of biomolecules. Bioinform. 25(16): 2110-2117 (2009) - [j43]Jens Christian Hühn, Eyke Hüllermeier:
FURIA: an algorithm for unordered fuzzy rule induction. Data Min. Knowl. Discov. 19(3): 293-319 (2009) - [j42]Eyke Hüllermeier, Michael M. Richter, Rosina Weber:
Prelude to the papers "Fuzzy case based reasoning for facial expression recognition" and "Temporal similarity by measuring possibilistic uncertainty in CBR". Fuzzy Sets Syst. 160(2): 212-213 (2009) - [j41]Yu Yi, Thomas Fober, Eyke Hüllermeier:
Fuzzy Operator Trees for Modeling Rating Functions. Int. J. Comput. Intell. Appl. 8(4): 413-428 (2009) - [j40]Eyke Hüllermeier, Ilya Vladimirskiy, Belén Prados-Suárez, Eva Stauch:
Supporting Case-Based Retrieval by Similarity Skyline. Künstliche Intell. 23(1): 24-29 (2009) - [j39]Weiwei Cheng, Eyke Hüllermeier:
Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn. 76(2-3): 211-225 (2009) - [j38]Jens C. Huhn, Eyke Hüllermeier:
FR3: A Fuzzy Rule Learner for Inducing Reliable Classifiers. IEEE Trans. Fuzzy Syst. 17(1): 138-149 (2009) - [j37]Eyke Hüllermeier, Stijn Vanderlooy:
Why Fuzzy Decision Trees are Good Rankers. IEEE Trans. Fuzzy Syst. 17(6): 1233-1244 (2009) - 2008
- [j36]Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng, Klaus Brinker:
Label ranking by learning pairwise preferences. Artif. Intell. 172(16-17): 1897-1916 (2008) - [j35]Eyke Hüllermeier, Klaus Brinker:
Learning valued preference structures for solving classification problems. Fuzzy Sets Syst. 159(18): 2337-2352 (2008) - [j34]Jens C. Huhn, Eyke Hüllermeier:
Is an ordinal class structure useful in classifier learning? Int. J. Data Min. Model. Manag. 1(1): 45-67 (2008) - [j33]Jürgen Beringer, Eyke Hüllermeier:
Case-based learning in a bipolar possibilistic framework. Int. J. Intell. Syst. 23(10): 1119-1134 (2008) - [j32]Stijn Vanderlooy, Eyke Hüllermeier:
A critical analysis of variants of the AUC. Mach. Learn. 72(3): 247-262 (2008) - [j31]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Klaus Brinker:
Multilabel classification via calibrated label ranking. Mach. Learn. 73(2): 133-153 (2008) - 2007
- [j30]Iman Karimi, Eyke Hüllermeier:
Risk assessment system of natural hazards: A new approach based on fuzzy probability. Fuzzy Sets Syst. 158(9): 987-999 (2007) - [j29]Jürgen Beringer, Eyke Hüllermeier:
Efficient instance-based learning on data streams. Intell. Data Anal. 11(6): 627-650 (2007) - [j28]Didier Dubois, Eyke Hüllermeier:
Comparing probability measures using possibility theory: A notion of relative peakedness. Int. J. Approx. Reason. 45(2): 364-385 (2007) - [j27]Eyke Hüllermeier, Frank Klawonn, Andreas Nürnberger:
Editorial. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 15(5) (2007) - [j26]Iman Karimi, Eyke Hüllermeier, Konstantin Meskouris:
A fuzzy-probabilistic earthquake risk assessment system. Soft Comput. 11(3): 229-238 (2007) - [j25]Ulrich Bodenhofer, Eyke Hüllermeier, Frank Klawonn, Rudolf Kruse:
Special Issue on Soft Computing for Information Mining. Soft Comput. 11(5): 397-399 (2007) - [j24]Nils Weskamp, Eyke Hüllermeier, Daniel Kuhn, Gerhard Klebe:
Multiple Graph Alignment for the Structural Analysis of Protein Active Sites. IEEE ACM Trans. Comput. Biol. Bioinform. 4(2): 310-320 (2007) - [j23]Eyke Hüllermeier:
Credible Case-Based Inference Using Similarity Profiles. IEEE Trans. Knowl. Data Eng. 19(6): 847-858 (2007) - [j22]Eyke Hüllermeier, Yu Yi:
In Defense of Fuzzy Association Analysis. IEEE Trans. Syst. Man Cybern. Part B 37(4): 1039-1043 (2007) - 2006
- [j21]Didier Dubois, Eyke Hüllermeier, Henri Prade:
A systematic approach to the assessment of fuzzy association rules. Data Min. Knowl. Discov. 13(2): 167-192 (2006) - [j20]Jürgen Beringer, Eyke Hüllermeier:
Online clustering of parallel data streams. Data Knowl. Eng. 58(2): 180-204 (2006) - [j19]Eyke Hüllermeier, Jürgen Beringer:
Learning from ambiguously labeled examples. Intell. Data Anal. 10(5): 419-439 (2006) - [j18]Didier Dubois, Eyke Hüllermeier, Henri Prade:
Fuzzy methods for case-based recommendation and decision support. J. Intell. Inf. Syst. 27(2): 95-115 (2006) - 2005
- [j17]Rajarajeswari Balasubramaniyan, Eyke Hüllermeier, Nils Weskamp, Jörg Kämper:
Clustering of gene expression data using a local shape-based similarity measure. Bioinform. 21(7): 1069-1077 (2005) - [j16]Eyke Hüllermeier:
Special issue on fuzzy sets in knowledge discovery. Fuzzy Sets Syst. 149(1): 1-3 (2005) - [j15]Eyke Hüllermeier:
Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy Sets Syst. 156(3): 387-406 (2005) - [j14]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Künstliche Intell. 19(1): 60- (2005) - [j13]Eyke Hüllermeier:
Experience-Based Decision Making: A Satisficing Decision Tree Approach. IEEE Trans. Syst. Man Cybern. Part A 35(5): 641-653 (2005) - 2004
- [j12]Nils Weskamp, Daniel Kuhn, Eyke Hüllermeier, Gerhard Klebe:
Efficient similarity search in protein structure databases by k-clique hashing. Bioinform. 20(10): 1522-1526 (2004) - [j11]Eyke Hüllermeier:
Flexible constraints for regularization in learning from data. Int. J. Intell. Syst. 19(6): 525-541 (2004) - [j10]Eyke Hüllermeier:
26. Jahrestagung Künstliche Intelligenz (KI-2003). Künstliche Intell. 18(1): 70- (2004) - 2003
- [j9]Eyke Hüllermeier:
Possibilistic instance-based learning. Artif. Intell. 148(1-2): 335-383 (2003) - [j8]Martine de Calmès, Didier Dubois, Eyke Hüllermeier, Henri Prade, Florence Sèdes:
Flexibility and Fuzzy Case-Based Evaluation in Querying: An Illustration in an Experimental Setting. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 11(1): 43-66 (2003) - [j7]Didier Dubois, Eyke Hüllermeier, Henri Prade:
On the representation of fuzzy rules in terms of crisp rules. Inf. Sci. 151: 301-326 (2003) - 2002
- [j6]Didier Dubois, Eyke Hüllermeier, Henri Prade:
Fuzzy set-based methods in instance-based reasoning. IEEE Trans. Fuzzy Syst. 10(3): 322-332 (2002) - [j5]Eyke Hüllermeier, Didier Dubois, Henri Prade:
Model adaptation in possibilistic instance-based reasoning. IEEE Trans. Fuzzy Syst. 10(3): 333-339 (2002) - 2001
- [j4]Eyke Hüllermeier:
Similarity-based inference as evidential reasoning. Int. J. Approx. Reason. 26(2): 67-100 (2001) - 1999
- [j3]Eyke Hüllermeier:
Approximation of uncertain functional relationships. Fuzzy Sets Syst. 101(2): 227-240 (1999) - [j2]Eyke Hüllermeier:
Numerical Methods for Fuzzy Initial Value Problems. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 7(5): 439-461 (1999) - 1997
- [j1]Eyke Hüllermeier:
An Approach to Modelling and Simulation of Uncertain Dynamical Systems. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 5(2): 117-138 (1997)
Conference and Workshop Papers
- 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 - 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 - 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 - 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 - 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 - 2019
- [c155]Vitalik Melnikov, Eyke Hüllermeier:
Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. ACML 2019: 1110-1125 - [c154]Thomas Mortier, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier, Willem Waegeman:
Set-Valued Prediction in Multi-Class Classification. BNAIC/BENELEARN 2019 - [c153]Vu-Linh Nguyen, Sébastien Destercke, Eyke Hüllermeier:
Epistemic Uncertainty Sampling. DS 2019: 72-86 - [c152]Mohsen Ahmadi Fahandar, Eyke Hüllermeier:
Feature Selection for Analogy-Based Learning to Rank. DS 2019: 279-289 - [c151]Felix Mohr, Marcel Wever, Alexander Tornede, Eyke Hüllermeier:
From Automated to On-The-Fly Machine Learning. GI-Jahrestagung 2019: 273-274 - [c150]Mohsen Ahmadi Fahandar, Eyke Hüllermeier:
Analogy-Based Preference Learning with Kernels. KI 2019: 34-47 - [c149]Klaus Brinker, Eyke Hüllermeier:
A Reduction of Label Ranking to Multiclass Classification. ECML/PKDD (3) 2019: 204-219 - [c148]Eyke Hüllermeier, Sébastien Destercke, Inés Couso:
Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. SUM 2019: 266-279 - 2018
- [c147]Felix Mohr, Marcel Wever, Eyke Hüllermeier:
On-the-Fly Service Construction with Prototypes. SCC 2018: 225-232 - [c146]Felix Mohr, Marcel Wever, Eyke Hüllermeier, Amin Faez:
(WIP) Towards the Automated Composition of Machine Learning Services. SCC 2018: 241-244 - [c145]Mohsen Ahmadi Fahandar, Eyke Hüllermeier:
Learning to Rank Based on Analogical Reasoning. AAAI 2018: 2951-2958 - [c144]Dirk Schäfer, Eyke Hüllermeier:
Preference-Based Reinforcement Learning Using Dyad Ranking. DS 2018: 161-175 - [c143]Marcel Wever, Felix Mohr, Eyke Hüllermeier:
Ensembles of evolved nested dichotomies for classification. GECCO 2018: 561-568 - [c142]Adil El Mesaoudi-Paul, Eyke Hüllermeier, Róbert Busa-Fekete:
Ranking Distributions based on Noisy Sorting. ICML 2018: 3469-3477 - [c141]Felix Mohr, Marcel Wever, Eyke Hüllermeier:
Reduction Stumps for Multi-class Classification. IDA 2018: 225-237 - [c140]Vu-Linh Nguyen, Sébastien Destercke, Marie-Hélène Masson, Eyke Hüllermeier:
Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. IJCAI 2018: 5089-5095 - 2017
- [c139]Eyke Hüllermeier:
Keynote 4: "Large-scale machine learning and extreme classification". CoDIT 2017: 16 - [c138]Ralph Ewerth, Matthias Springstein, Eric Müller, Alexander Balz, Jan Gehlhaar, Tolga Naziyok, Krzysztof Dembczynski, Eyke Hüllermeier:
Estimating relative depth in single images via rankboost. ICME 2017: 919-924 - [c137]Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso:
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. ICML 2017: 1078-1087 - [c136]Felix Mohr, Theo Lettmann, Eyke Hüllermeier:
Planning with Independent Task Networks. KI 2017: 193-206 - [c135]Nina Seemann, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke, Eyke Hüllermeier:
Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. LaTeCH@ACL 2017: 40-45 - [c134]Ammar Shaker, Waleri Heldt, Eyke Hüllermeier:
Learning TSK Fuzzy Rules from Data Streams. ECML/PKDD (2) 2017: 559-574 - [c133]Mike Czech, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim:
Predicting rankings of software verification tools. SWAN@ESEC/SIGSOFT FSE 2017: 23-26 - [c132]Inés Couso, Didier Dubois, Eyke Hüllermeier:
Maximum Likelihood Estimation and Coarse Data. SUM 2017: 3-16 - 2016
- [c131]Aulon Shabani, Adil Paul, Radu Platon, Eyke Hüllermeier:
Predicting the Electricity Consumption of Buildings: An Improved CBR Approach. ICCBR 2016: 356-369 - [c130]Kalina Jasinska, Krzysztof Dembczynski, Róbert Busa-Fekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hüllermeier:
Extreme F-measure Maximization using Sparse Probability Estimates. ICML 2016: 1435-1444 - [c129]Karlson Pfannschmidt, Eyke Hüllermeier, Susanne Held, Reto Neiger:
Evaluating Tests in Medical Diagnosis: Combining Machine Learning with Game-Theoretical Concepts. IPMU (1) 2016: 450-461 - [c128]Dirk Schäfer, Eyke Hüllermeier:
Plackett-Luce Networks for Dyad Ranking. LWDA 2016: 323-334 - [c127]Krzysztof Dembczynski, Wojciech Kotlowski, Willem Waegeman, Róbert Busa-Fekete, Eyke Hüllermeier:
Consistency of Probabilistic Classifier Trees. ECML/PKDD (2) 2016: 511-526 - [c126]Vitalik Melnikov, Eyke Hüllermeier:
Learning to Aggregate Using Uninorms. ECML/PKDD (2) 2016: 756-771 - 2015
- [c125]Amira Abdel-Aziz, Eyke Hüllermeier:
Case Base Maintenance in Preference-Based CBR. ICCBR 2015: 1-14 - [c124]Adil Paul, Eyke Hüllermeier:
A CBR Approach to the Angry Birds Game. ICCBR (Workshops) 2015: 68-77 - [c123]Balázs Szörényi, Róbert Busa-Fekete, Paul Weng, Eyke Hüllermeier:
Qualitative Multi-Armed Bandits: A Quantile-Based Approach. ICML 2015: 1660-1668 - [c122]Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier:
Online F-Measure Optimization. NIPS 2015: 595-603 - [c121]Balázs Szörényi, Róbert Busa-Fekete, Adil Paul, Eyke Hüllermeier:
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach. NIPS 2015: 604-612 - [c120]Dirk Schäfer, Eyke Hüllermeier:
Preference-Based Meta-Learning using Dyad Ranking: Recommending Algorithms in Cold-Start Situations. MetaSel@PKDD/ECML 2015: 110-111 - [c119]Dirk Schäfer, Eyke Hüllermeier:
Dyad Ranking Using A Bilinear Plackett-Luce Model. ECML/PKDD (2) 2015: 227-242 - [c118]Eyke Hüllermeier, Weiwei Cheng:
Superset Learning Based on Generalized Loss Minimization. ECML/PKDD (2) 2015: 260-275 - [c117]Sascha Henzgen, Eyke Hüllermeier:
Weighted Rank Correlation: A Flexible Approach Based on Fuzzy Order Relations. ECML/PKDD (2) 2015: 422-437 - 2014
- [c116]Róbert Busa-Fekete, Balázs Szörényi, Eyke Hüllermeier:
PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. AAAI 2014: 1701-1707 - [c115]Róbert Busa-Fekete, Eyke Hüllermeier:
A Survey of Preference-Based Online Learning with Bandit Algorithms. ALT 2014: 18-39 - [c114]Sascha Henzgen, Eyke Hüllermeier:
Mining Rank Data. Discovery Science 2014: 123-134 - [c113]Ali Fallah Tehrani, Marc Strickert, Eyke Hüllermeier:
The Choquet kernel for monotone data. ESANN 2014 - [c112]Amira Abdel-Aziz, Marc Strickert, Eyke Hüllermeier:
Learning Solution Similarity in Preference-Based CBR. ICCBR 2014: 17-31 - [c111]Róbert Busa-Fekete, Eyke Hüllermeier, Balázs Szörényi:
Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. ICML 2014: 1071-1079 - [c110]Dirk Schäfer, Eyke Hüllermeier:
Dyad Ranking Using a Bilinear Plackett-Luce Model. LWA 2014: 32-33 - 2013
- [c109]Sascha Henzgen, Marc Strickert, Eyke Hüllermeier:
Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems. CORES 2013: 279-288 - [c108]Ammar Shaker, Eyke Hüllermeier:
Recovery Analysis for Adaptive Learning from Non-stationary Data Streams. CORES 2013: 289-298 - [c107]György Szarvas, Róbert Busa-Fekete, Eyke Hüllermeier:
Learning to Rank Lexical Substitutions. EMNLP 2013: 1926-1932 - [c106]Ali Fallah Tehrani, Eyke Hüllermeier:
Ordinal Choquistic Regression. EUSFLAT Conf. 2013 - [c105]Amira Abdel-Aziz, Weiwei Cheng, Marc Strickert, Eyke Hüllermeier:
Preference-Based CBR: A Search-Based Problem Solving Framework. ICCBR 2013: 1-14 - [c104]Róbert Busa-Fekete, Balázs Szörényi, Weiwei Cheng, Paul Weng, Eyke Hüllermeier:
Top-k Selection based on Adaptive Sampling of Noisy Preferences. ICML (3) 2013: 1094-1102 - [c103]Krzysztof Dembczynski, Arkadiusz Jachnik, Wojciech Kotlowski, Willem Waegeman, Eyke Hüllermeier:
Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. ICML (3) 2013: 1130-1138 - [c102]Maryam Nasiri, Thomas Fober, Robin Senge, Eyke Hüllermeier:
Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing. IFSA/NAFIPS 2013: 715-721 - [c101]Eyke Hüllermeier, Weiwei Cheng:
Preference-Based CBR: General Ideas and Basic Principles. IJCAI 2013: 3012-3016 - [c100]Eyke Hüllermeier:
Learning from Imprecise and Fuzzy Data: on the Notion of Data Disambiguation. IJCCI 2013: IS-11 - [c99]Weiwei Cheng, Sascha Henzgen, Eyke Hüllermeier:
Labelwise versus Pairwise Decomposition in Label Ranking. LWA 2013: 129-136 - [c98]Robin Senge, Juan José del Coz, Eyke Hüllermeier:
Rectifying Classifier Chains for Multi-Label Classification. LWA 2013: 151-158 - [c97]Marc Strickert, Eyke Hüllermeier:
Neighbor Embedding by Soft Kendall Correlation. EuroVis (Short Papers) 2013 - 2012
- [c96]Matthias Leinweber, Lars Baumgärtner, Marco Mernberger, Thomas Fober, Eyke Hüllermeier, Gerhard Klebe, Bernd Freisleben:
GPU-based Cloud computing for comparing the structure of protein binding sites. DEST 2012: 1-6 - [c95]Krzysztof Dembczynski, Willem Waegeman, Eyke Hüllermeier:
An Analysis of Chaining in Multi-Label Classification. ECAI 2012: 294-299 - [c94]Robin Senge, Juan José del Coz, Eyke Hüllermeier:
On the Problem of Error Propagation in Classifier Chains for Multi-label Classification. GfKl 2012: 163-170 - [c93]Krzysztof Dembczynski, Wojciech Kotlowski, Eyke Hüllermeier:
Consistent Multilabel Ranking through Univariate Losses. ICML 2012 - [c92]Eyke Hüllermeier, Ali Fallah Tehrani:
On the VC-Dimension of the Choquet Integral. IPMU (1) 2012: 42-50 - [c91]Weiwei Cheng, Eyke Hüllermeier, Willem Waegeman, Volkmar Welker:
Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. NIPS 2012: 2510-2518 - [c90]Weiwei Cheng, Eyke Hüllermeier:
Probability Estimation for Multi-class Classification Based on Label Ranking. ECML/PKDD (2) 2012: 83-98 - [c89]Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier, Adrian Jaroszewicz, Willem Waegeman:
F-Measure Maximization in Topical Classification. RSCTC 2012: 439-446 - 2011
- [c88]Eyke Hüllermeier, Johannes Fürnkranz:
Learning from Label Preferences. ALT 2011: 38 - [c87]Eyke Hüllermeier, Johannes Fürnkranz:
Learning from Label Preferences. Discovery Science 2011: 2-17 - [c86]Edwin Lughofer, Eyke Hüllermeier:
On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure. EUSFLAT Conf. 2011: 380-387 - [c85]Ali Fallah Tehrani, Weiwei Cheng, Eyke Hüllermeier:
Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral. EUSFLAT Conf. 2011: 868-875 - [c84]Eyke Hüllermeier, Patrice Schlegel:
Preference-Based CBR: First Steps toward a Methodological Framework. ICCBR 2011: 77-91 - [c83]Wojciech Kotlowski, Krzysztof Dembczynski, Eyke Hüllermeier:
Bipartite Ranking through Minimization of Univariate Loss. ICML 2011: 1113-1120 - [c82]Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Learning Monotone Nonlinear Models using the Choquet Integral. LWA 2011: 81-88 - [c81]Ammar Shaker, Robin Senge, Eyke Hüllermeier:
Evolving Fuzzy Pattern Trees for Binary Classification on Data Streams. LWA 2011: 106-113 - [c80]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
An Exact Algorithm for F-Measure Maximization. NIPS 2011: 1404-1412 - [c79]Weiwei Cheng, Johannes Fürnkranz, Eyke Hüllermeier, Sang-Hyeun Park:
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. ECML/PKDD (1) 2011: 312-327 - [c78]Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Learning Monotone Nonlinear Models Using the Choquet Integral. ECML/PKDD (3) 2011: 414-429 - 2010
- [c77]Thomas Fober, Eyke Hüllermeier:
Similarity measures for protein structures based on fuzzy histogram comparison. FUZZ-IEEE 2010: 1-7 - [c76]Robin Senge, Eyke Hüllermeier:
Pattern trees for regression and fuzzy systems modeling. FUZZ-IEEE 2010: 1-7 - [c75]Thomas Fober, Marco Mernberger, Gerhard Klebe, Eyke Hüllermeier:
Efficient Similarity Retrieval of Protein Binding Sites based on Histogram Comparison. GCB 2010: 51-59 - [c74]Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Label Ranking Methods based on the Plackett-Luce Model. ICML 2010: 215-222 - [c73]Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Graded Multilabel Classification: The Ordinal Case. ICML 2010: 223-230 - [c72]Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier:
Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains. ICML 2010: 279-286 - [c71]Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier:
Graded Multilabel Classification: The Ordinal Case. LWA 2010: 39-45 - [c70]Weiwei Cheng, Michaël Rademaker, Bernard De Baets, Eyke Hüllermeier:
Predicting Partial Orders: Ranking with Abstention. ECML/PKDD (1) 2010: 215-230 - [c69]Krzysztof Dembczynski, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier:
Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss. ECML/PKDD (1) 2010: 280-295 - [c68]Hyung-Won Koh, Eyke Hüllermeier:
Mining Gradual Dependencies Based on Fuzzy Rank Correlation. SMPS 2010: 379-386 - [c67]Eyke Hüllermeier:
Uncertainty in Clustering and Classification. SUM 2010: 16-19 - 2009
- [c66]Eyke Hüllermeier, Maria Rifqi:
A Fuzzy Variant of the Rand Index for Comparing Clustering Structures. IFSA/EUSFLAT Conf. 2009: 1294-1298 - [c65]Thomas Fober, Eyke Hüllermeier:
Fuzzy Modeling of Labeled Point Cloud Superposition for the Comparison of Protein Binding Sites. IFSA/EUSFLAT Conf. 2009: 1299-1304 - [c64]Thomas Fober, Marco Mernberger, Ralph Moritz, Eyke Hüllermeier:
Graph-Kernels for the Comparative Analysis of Protein Active Sites. GCB 2009: 21-31 - [c63]Weiwei Cheng, Jens C. Huhn, Eyke Hüllermeier:
Decision tree and instance-based learning for label ranking. ICML 2009: 161-168 - [c62]Imen Boukhris, Zied Elouedi, Thomas Fober, Marco Mernberger, Eyke Hüllermeier:
Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection. ISDA 2009: 1245-1250 - [c61]Thomas Fober, Gerhard Klebe, Eyke Hüllermeier:
Efficient Construction of Multiple Geometrical Alignments for the Comparison of Protein Binding Sites. ISDA 2009: 1251-1256 - [c60]Weiwei Cheng, Eyke Hüllermeier:
A New Instance-Based Label Ranking Approach Using the Mallows Model. ISNN (1) 2009: 707-716 - [c59]Robin Senge, Eyke Hüllermeier:
Learning Pattern Tree Classifiers Using a Co-Evolutionary Algorithm. LWA 2009: KDML:105-110 - [c58]Weiwei Cheng, Eyke Hüllermeier:
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. LWA 2009: KDML:22-29 - [c57]Thomas Fober, Marco Mernberger, Vitalik Melnikov, Ralph Moritz, Eyke Hüllermeier:
Extension and Empirical Comparison of Graph-Kernels for the Analysis of Protein Active Sites. LWA 2009: KDML:30-36 - [c56]Weiwei Cheng, Eyke Hüllermeier:
Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. ECML/PKDD (1) 2009: 6 - [c55]Johannes Fürnkranz, Eyke Hüllermeier, Stijn Vanderlooy:
Binary Decomposition Methods for Multipartite Ranking. ECML/PKDD (1) 2009: 359-374 - 2008
- [c54]Eyke Hüllermeier, Michael M. Richter, Rosina Weber, Kerstin Bach, Miltos Petridis:
Preface: Uncertainty, Similarity, and Knowledge Discovery in CBR. ECCBR Workshops 2008: 117-118 - [c53]Weiwei Cheng, Eyke Hüllermeier:
Learning Similarity Functions from Qualitative Feedback. ECCBR 2008: 120-134 - [c52]Weiwei Cheng, Eyke Hüllermeier:
Instance-Based Label Ranking using the Mallows Model. ECCBR Workshops 2008: 143-157 - [c51]Eyke Hüllermeier, Ilya Vladimirskiy, Belén Prados-Suárez, Eva Stauch:
Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts and Extensions. ECCBR 2008: 240-254 - [c50]Thomas Fober, Eyke Hüllermeier, Marco Mernberger:
Evolutionary Construction of Multiple Graph Alignments for the Structural Analysis of Biomolecules. German Conference on Bioinformatics 2008: 44-53 - [c49]Thomas Fober, Eyke Hüllermeier, Marco Mernberger:
Evolutionary Construction of Multiple Graph Alignments for Mining Structured Biomolecular Data. LWA 2008: 27-33 - [c48]Eyke Hüllermeier, Stijn Vanderlooy:
Weighted Voting as Approximate MAP Prediction in Pairwise Classification. LWA 2008: 34-41 - [c47]Stijn Vanderlooy, Eyke Hüllermeier:
A Critical Analysis of Variants of the AUC. ECML/PKDD (1) 2008: 13 - 2007
- [c46]Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier:
On Pairwise Naive Bayes Classifiers. ECML 2007: 371-381 - [c45]Eyke Hüllermeier, Johannes Fürnkranz:
On Minimizing the Position Error in Label Ranking. ECML 2007: 583-590 - [c44]Eyke Hüllermeier, Klaus Brinker:
Fuzzy-Relational Classification: Combining Pairwise Decomposition Techniques with Fuzzy Preference Modeling. EUSFLAT Conf. (1) 2007: 353-360 - [c43]Jürgen Beringer, Eyke Hüllermeier:
Adaptive Optimization of the Number of Clusters in Fuzzy Clustering. FUZZ-IEEE 2007: 1-6 - [c42]Eyke Hüllermeier, Nils Weskamp, Gerhard Klebe, Daniel Kuhn:
Graph Alignment: Fuzzy Pattern Mining for the Structural Analysis of Protein Active Sites. FUZZ-IEEE 2007: 1-6 - [c41]Klaus Brinker, Eyke Hüllermeier:
Label Ranking in Case-Based Reasoning. ICCBR 2007: 77-91 - [c40]Klaus Brinker, Eyke Hüllermeier:
Case-Based Multilabel Ranking. IJCAI 2007: 702-707 - [c39]Jürgen Beringer, Eyke Hüllermeier:
An Efficient Algorithm for Instance-Based Learning on Data Streams. ICDM 2007: 34-48 - [c38]Weiwei Cheng, Eyke Hüllermeier, Bernhard Seeger, Ilya Vladimirskiy:
Interactive Ranking of Skylines Using Machine Learning Techniques. LWA 2007: 141-148 - 2006
- [c37]Klaus Brinker, Johannes Fürnkranz, Eyke Hüllermeier:
A Unified Model for Multilabel Classification and Ranking. ECAI 2006: 489-493 - [c36]Klaus Brinker, Eyke Hüllermeier:
Case-Based Label Ranking. ECML 2006: 566-573 - [c35]Korinna Bade, Eyke Hüllermeier, Andreas Nürnberger:
Hierarchical Classification by Expected Utility Maximization. ICDM 2006: 43-52 - 2005
- [c34]Didier Dubois, Eyke Hüllermeier:
A Notion of Comparative Probabilistic Entropy Based on the Possibilistic Specificity Ordering. ECSQARU 2005: 848-859 - [c33]Edwin Lughofer, Eyke Hüllermeier, Erich-Peter Klement:
Improving the interpretability of data-driven evolving fuzzy systems. EUSFLAT Conf. 2005: 28-33 - [c32]Yu Yi, Eyke Hüllermeier:
Learning Complexity-Bounded Rule-Based Classifiers by Combining. Association Analysis and Genetic Algorithms. EUSFLAT Conf. 2005: 47-52 - [c31]Eyke Hüllermeier, Jürgen Beringer:
Learning from Ambiguously Labeled Examples. IDA 2005: 168-179 - [c30]Eyke Hüllermeier, Johannes Fürnkranz:
Learning Label Preferences: Ranking Error Versus Position Error. IDA 2005: 180-191 - [c29]Eyke Hüllermeier:
Cho-k-NN: A Method for Combining Interacting Pieces of Evidence in Case-Based Learning. IJCAI 2005: 3-8 - [c28]Eyke Hüllermeier, Johannes Fürnkranz, Jürgen Beringer:
On Position Error and Label Ranking through Iterated Choice. LWA 2005: 158-163 - 2004
- [c27]Eyke Hüllermeier:
Instance-Based Prediction with Guaranteed Confidence. ECAI 2004: 97-101 - [c26]Eyke Hüllermeier, Johannes Fürnkranz:
Ranking by pairwise comparison a note on risk minimization. FUZZ-IEEE 2004: 97-102 - [c25]Eyke Hüllermeier:
Fuzzy Methods in Knowledge Discovery. Fuzzy Days 2004: 483 - [c24]Nils Weskamp, Eyke Hüllermeier, Daniel Kuhn, Gerhard Klebe:
Graph Alignments: A New Concept to Detect Conserved Regions in Protein Active Sites. German Conference on Bioinformatics 2004: 131-140 - 2003
- [c23]Johannes Fürnkranz, Eyke Hüllermeier:
Pairwise Preference Learning and Ranking. ECML 2003: 145-156 - [c22]Eyke Hüllermeier:
Instance-based collaborative filtering with fuzzy labels. EUSFLAT Conf. 2003: 468-473 - [c21]Nils Weskamp, Daniel Kuhn, Eyke Hüllermeier, Gerhard Klebe:
Efficient Similarity Search in Protein Structure Databases: Improving Cliqae-Detection through Clique Hashing. German Conference on Bioinformatics 2003: 179-184 - [c20]Eyke Hüllermeier:
Regularized Learning with Flexible Constraints. IDA 2003: 13-24 - [c19]Didier Dubois, Eyke Hüllermeier, Henri Prade:
A Note on Quality Measures for Fuzzy Asscociation Rules. IFSA 2003: 346-353 - [c18]Eyke Hüllermeier:
Inducing Fuzzy Concepts through Extended Version Space Learning. IFSA 2003: 677-684 - [c17]Eyke Hüllermeier:
Instance-Based Learning of Credible Label Sets. KI 2003: 450-464 - 2002
- [c16]Eyke Hüllermeier:
On the Representation and Combination of Evidence in Instance-Based Learning. ECAI 2002: 360-364 - [c15]Eyke Hüllermeier:
Possibilistic Induction in Decision-Tree Learning. ECML 2002: 173-184 - [c14]Eyke Hüllermeier:
Exploiting similarity and experience in decision making. FUZZ-IEEE 2002: 729-734 - [c13]Martine de Calmès, Didier Dubois, Eyke Hüllermeier, Henri Prade, Florence Sèdes:
Case-based querying and prediction: a fuzzy set approach. FUZZ-IEEE 2002: 735-739 - [c12]Martine de Calmès, Didier Dubois, Eyke Hüllermeier, Henri Prade, Florence Sèdes:
A Fuzzy Approach to Flexible Case-based Querying: Methodology and Experimentation. KR 2002: 449-458 - [c11]Eyke Hüllermeier:
Association Rules for Expressing Gradual Dependencies. PKDD 2002: 200-211 - 2001
- [c10]Eyke Hüllermeier:
Fuzzy Association Rules: Semantic Issues and Quality Measures. Fuzzy Days 2001: 380-391 - [c9]Eyke Hüllermeier:
Implication-Based Fuzzy Association Rules. PKDD 2001: 241-252 - 2000
- [c8]Eyke Hüllermeier:
Change Detection in Heuristic Search. AAAI/IAAI 2000: 898-903 - [c7]Eyke Hüllermeier:
Focusing Search by Using Problem Solving Experience. ECAI 2000: 50-54 - [c6]Eyke Hüllermeier:
Similarity-based Inference as Evitential Reasoning. ECAI 2000: 55-59 - [c5]Didier Dubois, Eyke Hüllermeier, Henri Prade:
Flexible Control of Case-Based Prediction in the Framework of Possibility Theory. EWCBR 2000: 61-73 - [c4]Eyke Hüllermeier:
A Method for Predicting Solutions in Case-Based Problem Solving. EWCBR 2000: 124-135 - 1999
- [c3]Eyke Hüllermeier:
A Possibilistic Formalization of Case-Based Reasoning and Decision Making. Fuzzy Days 1999: 411-420 - [c2]Eyke Hüllermeier:
Exploiting Similarity for Supporting Data Analysis and Problem Solving. IDA 1999: 257-268 - [c1]Eyke Hüllermeier:
Toward a Probabilistic Formalization of Case-Based Inference. IJCAI 1999: 248-253
Parts in Books or Collections
- 2013
- [p8]Thomas Fober, Gerhard Klebe, Eyke Hüllermeier:
Local Clique Merging: An Extension of the Maximum Common Subgraph Measure with Applications in Structural Bioinformatics. Algorithms from and for Nature and Life 2013: 279-286 - 2012
- [p7]Eyke Hüllermeier:
Fuzzy Rules in Data Mining: From Fuzzy Associations to Gradual Dependencies. Combining Experimentation and Theory 2012: 123-135 - 2010
- [p6]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning: An Introduction. Preference Learning 2010: 1-17 - [p5]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning and Ranking by Pairwise Comparison. Preference Learning 2010: 65-82 - [p4]Jens Christian Hühn, Eyke Hüllermeier:
An Analysis of the FURIA Algorithm for Fuzzy Rule Induction. Advances in Machine Learning I 2010: 321-344 - 2008
- [p3]Eyke Hüllermeier:
Fuzzy Methods for Data Mining and Machine Learning: State ofthe Art and Prospects. Fuzzy Sets and Their Extensions: Representation, Aggregation and Models 2008: 357-375 - 2003
- [p2]Eyke Hüllermeier:
Sequential Decision Making in Heuristic Search. Planning Based on Decision Theory 2003: 27-41 - 2001
- [p1]Eyke Hüllermeier, Didier Dubois, Henri Prade:
Formalizing Case Based Inference Using Fuzzy Rules. Soft Computing in Case Based Reasoning 2001: 47-72
Editorship
- 2024
- [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] - 2022
- [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] - 2016
- [e11]Gal A. Kaminka, Maria Fox, Paolo Bouquet, Eyke Hüllermeier, Virginia Dignum, Frank Dignum, Frank van Harmelen:
ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016). Frontiers in Artificial Intelligence and Applications 285, IOS Press 2016, ISBN 978-1-61499-671-2 [contents] - 2015
- [e10]Eyke Hüllermeier, Mirjam Minor:
Case-Based Reasoning Research and Development - 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28-30, 2015, Proceedings. Lecture Notes in Computer Science 9343, Springer 2015, ISBN 978-3-319-24585-0 [contents] - 2014
- [e9]Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I. Lecture Notes in Computer Science 8724, Springer 2014, ISBN 978-3-662-44847-2 [contents] - [e8]Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II. Lecture Notes in Computer Science 8725, Springer 2014, ISBN 978-3-662-44850-2 [contents] - [e7]Toon Calders, Floriana Esposito, Eyke Hüllermeier, Rosa Meo:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part III. Lecture Notes in Computer Science 8726, Springer 2014, ISBN 978-3-662-44844-1 [contents] - 2013
- [e6]Johannes Fürnkranz, Eyke Hüllermeier, Tomoyuki Higuchi:
Discovery Science - 16th International Conference, DS 2013, Singapore, October 6-9, 2013. Proceedings. Lecture Notes in Computer Science 8140, Springer 2013, ISBN 978-3-642-40896-0 [contents] - 2012
- [e5]Eyke Hüllermeier, Sebastian Link, Thomas Fober, Bernhard Seeger:
Scalable Uncertainty Management - 6th International Conference, SUM 2012, Marburg, Germany, September 17-19, 2012. Proceedings. Lecture Notes in Computer Science 7520, Springer 2012, ISBN 978-3-642-33361-3 [contents] - 2010
- [e4]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Springer 2010, ISBN 978-3-642-14124-9 [contents] - [e3]Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann:
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods - 13th International Conference, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, Part I. Communications in Computer and Information Science 80, Springer 2010, ISBN 978-3-642-14054-9 [contents] - [e2]Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann:
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications - 13th International Conference, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, Part II. Communications in Computer and Information Science 81, Springer 2010, ISBN 978-3-642-14057-0 [contents] - [e1]Eyke Hüllermeier, Rudolf Kruse, Frank Hoffmann:
Computational Intelligence for Knowledge-Based Systems Design, 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings. Lecture Notes in Computer Science 6178, Springer 2010, ISBN 978-3-642-14048-8 [contents]
Reference Works
- 2017
- [r4]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1000-1005 - [r3]Johannes Fürnkranz, Eyke Hüllermeier:
Rank Correlation. Encyclopedia of Machine Learning and Data Mining 2017: 1055 - 2010
- [r2]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Encyclopedia of Machine Learning 2010: 789-795 - 2009
- [r1]Eyke Hüllermeier:
Fuzzy Methods in Data Mining. Encyclopedia of Data Warehousing and Mining 2009: 907-912
Informal and Other Publications
- 2024
- [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
- [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
- [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
- [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
- [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]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. CoRR abs/2006.11916 (2020) - [i39]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. CoRR abs/2006.13346 (2020) - [i38]Clemens Damke, Vitalik Melnikov, Eyke Hüllermeier:
A Novel Higher-order Weisfeiler-Lehman Graph Convolution. CoRR abs/2007.00346 (2020) - [i37]Alexander Tornede, Marcel Wever, Stefan Werner, Felix Mohr, Eyke Hüllermeier:
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. CoRR abs/2007.02816 (2020) - [i36]Sadegh Abbaszadeh, Eyke Hüllermeier:
Machine Learning with the Sugeno Integral: The Case of Binary Classification. CoRR abs/2007.03046 (2020) - [i35]Karlson Pfannschmidt, Eyke Hüllermeier:
Learning Choice Functions via Pareto-Embeddings. CoRR abs/2007.06927 (2020) - [i34]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. CoRR abs/2007.08145 (2020) - [i33]Stefan Heid, Marcel Wever, Eyke Hüllermeier:
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction. CoRR abs/2008.01377 (2020) - [i32]Julian Lienen, Eyke Hüllermeier:
Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. CoRR abs/2010.13118 (2020) - [i31]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A Flexible Class of Dependence-aware Multi-Label Loss Functions. CoRR abs/2011.00792 (2020) - [i30]Viktor Bengs, Eyke Hüllermeier:
Multi-Armed Bandits with Censored Consumption of Resources. CoRR abs/2011.00813 (2020) - [i29]Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Towards Meta-Algorithm Selection. CoRR abs/2011.08784 (2020) - [i28]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Michael Rapp:
Learning Structured Declarative Rule Sets - A Challenge for Deep Discrete Learning. CoRR abs/2012.04377 (2020) - [i27]Georg Krempl, Vera Hofer, Geoffrey I. Webb, Eyke Hüllermeier:
Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372). Dagstuhl Reports 10(4): 1-36 (2020) - 2019
- [i26]Mohsen Ahmadi Fahandar, Eyke Hüllermeier:
Analogy-Based Preference Learning with Kernels. CoRR abs/1901.02001 (2019) - [i25]Karlson Pfannschmidt, Pritha Gupta, Eyke Hüllermeier:
Learning Choice Functions. CoRR abs/1901.10860 (2019) - [i24]Vu-Linh Nguyen, Eyke Hüllermeier:
Reliable Multi-label Classification: Prediction with Partial Abstention. CoRR abs/1904.09235 (2019) - [i23]Robin Senge, Juan José del Coz, Eyke Hüllermeier:
Rectifying Classifier Chains for Multi-Label Classification. CoRR abs/1906.02915 (2019) - [i22]Thomas Mortier, Marek Wydmuch, Eyke Hüllermeier, Krzysztof Dembczynski, Willem Waegeman:
Efficient Algorithms for Set-Valued Prediction in Multi-Class Classification. CoRR abs/1906.08129 (2019) - [i21]Viktor Bengs, Eyke Hüllermeier:
Preselection Bandits under the Plackett-Luce Model. CoRR abs/1907.06123 (2019) - [i20]Vu-Linh Nguyen, Sébastien Destercke, Eyke Hüllermeier:
Epistemic Uncertainty Sampling. CoRR abs/1909.00218 (2019) - [i19]Eyke Hüllermeier, Willem Waegeman:
Aleatoric and Epistemic Uncertainty in Machine Learning: A Tutorial Introduction. CoRR abs/1910.09457 (2019) - [i18]Ammar Shaker, Eyke Hüllermeier:
TSK-Streams: Learning TSK Fuzzy Systems on Data Streams. CoRR abs/1911.03951 (2019) - 2018
- [i17]Karlson Pfannschmidt, Pritha Gupta, Eyke Hüllermeier:
Deep architectures for learning context-dependent ranking functions. CoRR abs/1803.05796 (2018) - [i16]Sascha Henzgen, Eyke Hüllermeier:
Mining Rank Data. CoRR abs/1806.05897 (2018) - [i15]Róbert Busa-Fekete, Eyke Hüllermeier, Adil El Mesaoudi-Paul:
Preference-based Online Learning with Dueling Bandits: A Survey. CoRR abs/1807.11398 (2018) - [i14]Felix Mohr, Marcel Wever, Eyke Hüllermeier:
Automated Machine Learning Service Composition. CoRR abs/1809.00486 (2018) - [i13]Willem Waegeman, Krzysztof Dembczynski, Eyke Hüllermeier:
Multi-Target Prediction: A Unifying View on Problems and Methods. CoRR abs/1809.02352 (2018) - [i12]Marcel Wever, Felix Mohr, Eyke Hüllermeier:
Automated Multi-Label Classification based on ML-Plan. CoRR abs/1811.04060 (2018) - [i11]Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp:
Learning Interpretable Rules for Multi-label Classification. CoRR abs/1812.00050 (2018) - 2017
- [i10]Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, Ke Yi:
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). CoRR abs/1701.09007 (2017) - [i9]Mike Czech, Eyke Hüllermeier, Marie-Christine Jakobs, Heike Wehrheim:
Predicting Rankings of Software Verification Competitions. CoRR abs/1703.00757 (2017) - [i8]Mohsen Ahmadi Fahandar, Eyke Hüllermeier:
Learning to Rank based on Analogical Reasoning. CoRR abs/1711.10207 (2017) - [i7]Eyke Hüllermeier:
From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection. CoRR abs/1712.00646 (2017) - [i6]Mohsen Ahmadi Fahandar, Eyke Hüllermeier, Inés Couso:
Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. CoRR abs/1712.01158 (2017) - 2014
- [i5]Michiel Stock, Thomas Fober, Eyke Hüllermeier, Serghei Glinca, Gerhard Klebe, Tapio Pahikkala, Antti Airola, Bernard De Baets, Willem Waegeman:
Identification of functionally related enzymes by learning-to-rank methods. CoRR abs/1405.4394 (2014) - [i4]Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, Scott Sanner:
Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports 4(3): 1-27 (2014) - 2013
- [i3]Eyke Hüllermeier:
Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization. CoRR abs/1305.0698 (2013) - [i2]Willem Waegeman, Krzysztof Dembczynski, Weiwei Cheng, Eyke Hüllermeier:
On the Bayes-optimality of F-measure maximizers. CoRR abs/1310.4849 (2013) - 2011
- [i1]Weiwei Cheng, Eyke Hüllermeier:
Label Ranking with Abstention: Predicting Partial Orders by Thresholding Probability Distributions (Extended Abstract). CoRR abs/1112.0508 (2011)
Coauthor Index
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Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-12-10 21:42 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
