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A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria · Study

WorkA General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria

Amy Greenwald2003

Work2003

A General Class of No-Regret Learning Algorithms and Game-Theoretic Equilibria

53 citations·0/0 files stored·Lecture notes in computer science

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Game Theory and Applications Game Theory and Voting Systems Advanced Bandit Algorithms Research

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 1995A desicion-theoretic generalization of on-line learning and an application to boosting 1960Contributions to the theory of games.1974Subjectivity and correlation in randomized strategies 2000A Simple Adaptive Procedure Leading to Correlated Equilibrium 1951An Iterative Method of Solving a Game 1956An analog of the minimax theorem for vector payoffs 1996Game theory, on-line prediction and boosting

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2020Comparing the Impact of Learning in Bidding Decision-Making Processes Using Algorithmic Game Theory

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Details

Year2003

Typeother

Published2003-01-01

VenueLecture notes in computer science

ISSN-L0302-9743

Languageen

Citations53

Files0 public / 0

ShelfGame Theory and Applications

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doi10.1007/978-3-540-45167-9_2

openalex_workW1531245405

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