Cost-sensitive Active Learning
Cost-Sensitive Active Learning is an active learning paradigm that, as a refinement of query selection strategies, incorporates the varying costs associated with acquiring different types of labels or querying different instances, thereby distinguishing itself from standard active learning by optimizing not only for model performance but also for cost efficiency.
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2.5K
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151
Authors
79
Institutions
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2017–2026
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Southwest Petroleum University
Chengdu, China
5 h-index
18 publications
487 citations
The University of Texas at Austin
Austin, United States
5 h-index
8 publications
480 citations
New York University
New York, United States
4 h-index
5 publications
269 citations

University of Trento
Trento, Italy
3 h-index
7 publications
233 citations
Microsoft (United States)
Redmond, United States
3 h-index
5 publications
175 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
4 publications
130 citations
Tencent (China)
Shenzhen, China
2 h-index
4 publications
148 citations

South China University of Technology
Guangzhou, China
2 h-index
10 publications
370 citations
1–8 of 79
Repository · 7 publications · 206 citations
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3 publications · 170 citations
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2 publications · 95 citations
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2 publications · 78 citations
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GRSL · 1 publications · 13 citations
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