CART random forests are analyzed as controlled stochastic processes, separating subsampling and split policy effects, with explicit MSE derivations for linear models showing local stabilization but potential global suboptimality.
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2 Pith papers cite this work, alongside 117 external citations. Polarity classification is still indexing.
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DecentMem is a decentralized dual-pool memory framework for self-evolving multi-agent systems that provides O(log T) regret guarantees and yields up to 23.8% accuracy gains over centralized baselines.
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CART Random Forests as Sequential Allocation over Random Opportunity Sets: A Stochastic-Control Theory of Ensemble Risk
CART random forests are analyzed as controlled stochastic processes, separating subsampling and split policy effects, with explicit MSE derivations for linear models showing local stabilization but potential global suboptimality.
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Self-Evolving Multi-Agent Systems via Decentralized Memory
DecentMem is a decentralized dual-pool memory framework for self-evolving multi-agent systems that provides O(log T) regret guarantees and yields up to 23.8% accuracy gains over centralized baselines.