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Optimizer as Detector: Stochastic Gradient Descent for Latent Mixture Models
Pooling latent subpopulations can obscure relationships and yield misleading regression conclusions, including Simpson's paradox (SP). We propose a detector based on the steady-state dynamics of constant-step stochastic gradient descent (SGD). Unlike likelihood-based mixture tests and confounder-sea
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