Preference-based Learning
Preference-Based Learning is a machine learning paradigm that belongs to the class of learning from human feedback, differing from other paradigms by specifically utilizing pairwise or ranked preferences to infer a reward function or policy that aligns with human judgments.
216
Publications
10.7K
Citations
569
Authors
302
Institutions
Publications per year
2017–2026
88
Parents
216
Label ranking by learning pairwise preferences
Eyke Hüllermeier, Johannes Fürnkranz, Weiwei Cheng et al. · Artificial Intelligence · 2008 · 536 citations
Preference learning with Gaussian processes
Wei Chu, Zoubin Ghahramani · 2005 · 362 citations
Active learning via transductive experimental design
Kai Yu, Jinbo Bi, Volker Tresp · 2006 · 356 citations
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569
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302

IT University of Copenhagen
Copenhagen, Denmark
7 h-index
15 publications
1.1K citations

Technische Universität Darmstadt
Darmstadt, Germany
7 h-index
17 publications
1.5K citations
Stanford University
Stanford, United States
7 h-index
15 publications
424 citations
Philipps University of Marburg
Marburg, Germany
6 h-index
11 publications
2.1K citations
University College London
London, United Kingdom
5 h-index
12 publications
1.7K citations
University of California, Berkeley
Berkeley, United States
5 h-index
10 publications
1.2K citations
University of Kentucky
Lexington, United States
4 h-index
7 publications
180 citations
University of Malta
Msida, Malta
4 h-index
9 publications
283 citations
1–8 of 302
Repository · 17 publications · 316 citations
12
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5 publications · 258 citations
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Conference · 4 publications · 154 citations
4
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JABA · 4 publications · 207 citations
4
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3 publications · 609 citations
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