No-regret Dynamics
No-Regret Dynamics is a learning framework in game theory whereby a player’s strategy update rule ensures that the long-term average regret of its actions converges to zero relative to the best fixed action in hindsight, under repeated interactions in a zero-sum game.
26
Publications
1.3K
Citations
65
Authors
39
Institutions
Publications per year
2017–2026
10
Variants
No-regret Learning
Parents
26
A Theory of Regret Regulation 1.1
Rik Pieters, Marcel Zeelenberg · Journal of Consumer Psychology · 2007 · 264 citations
Constantinos Daskalakis, Andrew Ilyas, Vasilis Syrgkanis et al. · International Conference on Learning Representations · 2017 · 138 citations
Jason R. Marden, Gürdal Arslan, Jeff S. Shamma · 2007 · 74 citations
1–5 of 26
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Singapore University of Technology and Design, Singapore
3 publications · 79 citations
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Centre National de la Recherche Scientifique
Paris, France
3 h-index
5 publications
108 citations
Carnegie Mellon University
Pittsburgh, United States
3 h-index
5 publications
75 citations
University of California, Berkeley
Berkeley, United States
2 h-index
4 publications
144 citations
Laboratoire d'Informatique de Grenoble
Grenoble, France
2 h-index
3 publications
50 citations
Singapore University of Technology and Design
Singapore, Singapore
2 h-index
4 publications
74 citations

Tilburg University
Tilburg, The Netherlands
2 h-index
4 publications
668 citations
Massachusetts Institute of Technology
Cambridge, United States
2 h-index
6 publications
462 citations
Politecnico di Milano
Milan, Italy
2 h-index
5 publications
70 citations
1–8 of 39
Repository · 4 publications · 78 citations
4
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2 publications · 125 citations
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JACM · 1 publications · 11 citations
1
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1 publications · 110 citations
1
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1 publications · 29 citations
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