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This paper connects score matching, maximum likelihood, and expectation-maximization in mixed linear regression, providing theoretical guarantees and analyzing gradient behavior under diffusion noise levels.
This paper identifies sufficient sample sizes for maximum-likelihood support recovery of sparse binary signals in high-SNR regimes, revealing an information-theoretic threshold and trade-offs between measurement sparsity and computational cost.