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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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.