Feedback Policies is a formal framework within adaptive feedback systems that specifies the rules and criteria governing the selection, timing, and customization of feedback to optimize learning outcomes based on individual learner states and performance trajectories.
126
Publications
5.4K
Citations
343
Authors
174
Institutions
Publications per year
2017–2026
46
Parents
Publications
126
Reinforcement learning in feedback control
Roland Hafner, Martin Riedmiller · Machine Learning · 2011 · 218 citations · Full text
Sourcing Innovation: On Feedback in Contests
Jürgen Mihm, Jochen Schlapp · Management Science · 2018 · 128 citations
1–5 of 126
Authors
343
- SL
4
h-index
- DC
4
h-index
- HC
4
h-index
- AA
3
h-index
- JL
3
h-index
1–5 of 343
Institutions
174
Massachusetts Institute of Technology
Cambridge, United States
8 h-index
16 publications
356 citations
University of California, Berkeley
Berkeley, United States
8 h-index
20 publications
807 citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
13 publications
671 citations
ETH Zurich
Zurich, Switzerland
5 h-index
10 publications
296 citations
Tsinghua University
Beijing, China
5 h-index
7 publications
360 citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
8 publications
213 citations
University of Maryland, College Park
College Park, United States
4 h-index
10 publications
408 citations
University of Pennsylvania
Philadelphia, United States
4 h-index
10 publications
328 citations
1–8 of 174
Venues
- IEEE Transactions on Automatic Control
6 publications · 230 citations
6
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- Management Science
4 publications · 352 citations
4
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- arXiv (Cornell University)
Repository · 4 publications · 75 citations
4
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- SIAM Journal on Control and Optimization
3 publications · 164 citations
3
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- Automatica
3 publications · 642 citations
3
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1–5