Sequential prediction passing on DAGs for logistic regression yields O(M/sqrt(D)) excess loss when M-agent windows cover all features, with Omega(k/D) lower bound identifying depth as the fundamental limit.
URL https://www.aeaweb.org/articles?id=10
3 Pith papers cite this work. Polarity classification is still indexing.
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Bee hive mind from weighted voter imitation equals a single RL agent using a new multi-armed bandit rule called Maynard-Cross Learning.
The paper proposes and analyzes a distributed perception mechanism in Friedkin-Johnsen networks that enables convergence to true social power through local interactions in static and reflected-appraisal settings.
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Networked Information Aggregation for Binary Classification
Sequential prediction passing on DAGs for logistic regression yields O(M/sqrt(D)) excess loss when M-agent windows cover all features, with Omega(k/D) lower bound identifying depth as the fundamental limit.
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The Hive Mind is a Single Reinforcement Learning Agent
Bee hive mind from weighted voter imitation equals a single RL agent using a new multi-armed bandit rule called Maynard-Cross Learning.
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Dynamical models for distributed social power perception in Friedkin-Johnsen influence networks
The paper proposes and analyzes a distributed perception mechanism in Friedkin-Johnsen networks that enables convergence to true social power through local interactions in static and reflected-appraisal settings.