Gradient EM converges exponentially to optimal population loss minimizers for agnostic fitting of k parametric functions under strong convexity and smoothness of the loss, proper initialization, and separation conditions.
Statistical guarantees for the em algorithm: From population to sample-based analysis.The Annals of Statistics, 45(1):77–120, 2017
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Expectation Maximization (EM) Converges for General Agnostic Mixtures
Gradient EM converges exponentially to optimal population loss minimizers for agnostic fitting of k parametric functions under strong convexity and smoothness of the loss, proper initialization, and separation conditions.