The paper provides novel lower bounds connecting L1 distances of mixture densities to discrepancies in mixing measures, leading to first contraction rates for Dirichlet process mixtures with unknown scale.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.
citing papers explorer
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Convergence Rates for Latent Mixing Measures in Infinite Homoscedastic Location-Scale Mixture Models
The paper provides novel lower bounds connecting L1 distances of mixture densities to discrepancies in mixing measures, leading to first contraction rates for Dirichlet process mixtures with unknown scale.
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Position: Agentic AI System Is a Foreseeable Pathway to AGI
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.