Active Learning From Demonstration
Active Learning From Demonstration is a machine learning framework within the paradigm of Learning From Demonstration that distinguishes itself by actively selecting informative demonstrations through query strategies to optimize learning efficiency and performance.
28
Publications
801
Citations
71
Authors
26
Institutions
Publications per year
2017–2026
16
Parents
28
Active Example Selection for In-Context Learning
Yiming Zhang, Feng Shi, Chenhao Tan · 2022 · 87 citations · Full text
Active Reward Learning from Critiques
Yuchen Cui, Scott Niekum · 2018 · 59 citations
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The University of Texas at Austin
Austin, United States
5 h-index
10 publications
333 citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
9 publications
387 citations
State Key Laboratory of Quantum Optics and Quantum Optics Devices
Taiyuan, China
1 h-index
4 publications
48 citations
University of California, Berkeley
Berkeley, United States
1 h-index
1 publications
23 citations
Meta (Israel)
Tel Aviv, Israel
1 h-index
1 publications
18 citations
Yale University
New Haven, United States
1 h-index
2 publications
94 citations
Suez Canal University
Ismailia, Egypt
1 h-index
4 publications
40 citations
Menlo School
Atherton, United States
1 h-index
2 publications
30 citations
1–8 of 26
Conference · 2 publications · 23 citations
2
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Conference · 1 publications · 17 citations
1
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1 publications · 27 citations
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Conference · 1 publications · 12 citations
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Repository · 1 publications · 53 citations
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