A unified bandit framework for general open multi-agent systems with global-UCB algorithms and regret bounds linear in entry uncertainty and dependent on system stability and agent patterns.
Journal of mathematics and mechanics , pages=
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.
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Bandit Learning in General Open Multi-agent Systems
A unified bandit framework for general open multi-agent systems with global-UCB algorithms and regret bounds linear in entry uncertainty and dependent on system stability and agent patterns.
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An adaptive variance estimator for relative sparsity
A new adaptive variance estimator for relative sparsity coefficients is introduced that fully utilizes the prior asymptotic normality theorem and incorporates variable selection effects.