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Connecting Score Matching, Maximum Likelihood, and Expectation-Maximization in Mixed Linear Regression
We study variance-preserving diffusion of the response in mixed linear regression (MLR) with unknown mixing weights. Our analysis separates the statistical guarantees of score matching from the loss geometry and optimization signal at a fixed diffusion noise level. The KL divergence links the denois
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