Regret-minimization algorithms for multi-agent cooperative learning systems
PulseAugur coverage of Regret-minimization algorithms for multi-agent cooperative learning systems — every cluster mentioning Regret-minimization algorithms for multi-agent cooperative learning systems across labs, papers, and developer communities, ranked by signal.
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New research explores 1/2-Tsallis-INF algorithm for best-arm identification
This research paper investigates the effectiveness of the 1/2-Tsallis-INF algorithm, a known method for minimizing regret in multi-armed bandit problems, when applied to the task of identifying the best arm. The study a…
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New spectral filtering approach for distributed online control analyzed
Researchers have developed a spectral filtering approach to analyze regret in distributed online control for linear dynamical systems. This method extends the Online Spectral Control framework to a distributed setting, …
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Blackwell Approachability and Gradient Equilibrium Shown Equivalent
Researchers have established an equivalence between Blackwell approachability and Gradient Equilibrium (GEQ), a framework for online optimization. This finding bridges GEQ with established online learning concepts like …