ChatGPT for good? On opportunities and challenges of large language models for education E Kasneci, K Seßler, S Küchemann, M Bannert, D Dementieva, F Fischer, ... Learning and individual differences 103, 102274, 2023 | 3442 | 2023 |
Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods E Hüllermeier, W Waegeman Machine learning 110 (3), 457-506, 2021 | 1544 | 2021 |
Multilabel classification via calibrated label ranking J Fürnkranz, E Hüllermeier, E Loza Mencía, K Brinker Machine learning 73, 133-153, 2008 | 1143 | 2008 |
Preference learning and ranking by pairwise comparison J Fürnkranz, E Hüllermeier Preference learning, 65-82, 2011 | 921 | 2011 |
Preference learning J Fürnkranz, E Hüllermeier Encyclopedia of Machine Learning, 789-795, 2010 | 914* | 2010 |
Label ranking by learning pairwise preferences E Hüllermeier, J Fürnkranz, W Cheng, K Brinker Artificial Intelligence 172 (16-17), 1897-1916, 2008 | 734 | 2008 |
Bayes optimal multilabel classification via probabilistic classifier chains W Cheng, E Hüllermeier, KJ Dembczynski Proceedings of the 27th international conference on machine learning (ICML …, 2010 | 649 | 2010 |
Combining instance-based learning and logistic regression for multilabel classification W Cheng, E Hüllermeier Machine Learning 76, 211-225, 2009 | 569 | 2009 |
FURIA: an algorithm for unordered fuzzy rule induction J Hühn, E Hüllermeier Data Mining and Knowledge Discovery 19, 293-319, 2009 | 567 | 2009 |
An approach to modelling and simulation of uncertain dynamical systems E Hüllermeier International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems …, 1997 | 521 | 1997 |
On label dependence and loss minimization in multi-label classification K Dembczyński, W Waegeman, W Cheng, E Hüllermeier Machine Learning 88, 5-45, 2012 | 500 | 2012 |
Open challenges for data stream mining research G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ... ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014 | 392 | 2014 |
Online clustering of parallel data streams J Beringer, E Hüllermeier Data & knowledge engineering 58 (2), 180-204, 2006 | 345 | 2006 |
Pairwise preference learning and ranking J Fürnkranz, E Hüllermeier European conference on machine learning, 145-156, 2003 | 337 | 2003 |
Learning from ambiguously labeled examples E Hüllermeier, J Beringer Intelligent Data Analysis 10 (5), 419-439, 2006 | 328 | 2006 |
Grouping, overlap, and generalized bientropic functions for fuzzy modeling of pairwise comparisons H Bustince, M Pagola, R Mesiar, E Hullermeier, F Herrera IEEE Transactions on Fuzzy Systems 20 (3), 405-415, 2011 | 321 | 2011 |
Fuzzy methods in machine learning and data mining: Status and prospects E Hüllermeier Fuzzy sets and Systems 156 (3), 387-406, 2005 | 291 | 2005 |
A systematic approach to the assessment of fuzzy association rules D Dubois, E Hüllermeier, H Prade Data Mining and Knowledge Discovery 13, 167-192, 2006 | 248 | 2006 |
Preferences in AI: An overview C Domshlak, E Hüllermeier, S Kaci, H Prade Artificial Intelligence 175 (7-8), 1037-1052, 2011 | 246 | 2011 |
ML-Plan: Automated machine learning via hierarchical planning F Mohr, M Wever, E Hüllermeier Machine Learning 107, 1495-1515, 2018 | 245 | 2018 |