Human-in-the-loop Learning
Human-In-The-Loop Learning is a machine learning paradigm in which human agents are actively integrated into the computational learning process to provide feedback, guidance, or intervention that directly informs model updates, distinguishing it from fully autonomous or unsupervised learning approaches by embedding human expertise, intent, or judgment as essential components of policy or behavior refinement.
315
Publications
20.1K
Citations
1.1K
Authors
435
Institutions
Publications per year
2017–2026
238
Variants
Hitl Learning
315
Cellpose 2.0: how to train your own model
Marius Pachitariu, Carsen Stringer · Nature Methods · 2022 · 1.4K citations · Full text
A survey of human-in-the-loop for machine learning
Xingjiao Wu, Luwei Xiao, Yixuan Sun et al. · Future Generation Computer Systems · 2022 · 669 citations · Full text
1–5 of 315
1.1K
7
h-index
5
h-index
4
h-index
4
h-index
4
h-index
1–5 of 1.1K
435
University of Washington
Seattle, United States
8 h-index
16 publications
1.2K citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
22 publications
545 citations
Georgia Institute of Technology
Atlanta, United States
7 h-index
8 publications
343 citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
6 h-index
16 publications
1.5K citations
Nanyang Technological University
Singapore, Singapore
5 h-index
6 publications
829 citations
Tsinghua University
Beijing, China
5 h-index
7 publications
105 citations

Technische Universität Darmstadt
Darmstadt, Germany
4 h-index
9 publications
227 citations

Cornell University
Ithaca, United States
4 h-index
9 publications
307 citations
1–8 of 435
Repository · 24 publications · 667 citations
15
h-index
Conference · 10 publications · 575 citations
10
h-index
4 publications · 170 citations
5
h-index
3 publications · 63 citations
3
h-index
3 publications · 185 citations
3
h-index
1–5