A gradient method alternating short GD steps and long Polyak steps achieves local linear convergence for overparameterized GMMs under mixture-weight assumptions.
Improved convergence guarantees for learning gaussian mixture models by EM and gradient EM
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Local linear convergence of gradient methods for overparameterized Gaussian mixtures
A gradient method alternating short GD steps and long Polyak steps achieves local linear convergence for overparameterized GMMs under mixture-weight assumptions.