Neural density estimators that factor over a known Markov random field achieve dimension-independent L1 rates n^{-1/(4+r)} (and optimally n^{-1/(2+r)}), where r is the maximum clique size.
Nearly tight sample complexity bounds for learning mixtures of gaussians via sample compression schemes
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Dimension-independent rates for structured neural density estimation
Neural density estimators that factor over a known Markov random field achieve dimension-independent L1 rates n^{-1/(4+r)} (and optimally n^{-1/(2+r)}), where r is the maximum clique size.