For strongly concave log-likelihoods, square-root (Cholesky) parametrization of Gaussian variational inference yields exponential convergence guarantees for both the natural-gradient flow and a discrete-time natural-gradient algorithm.
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Optimization Guarantees for Square-Root Natural-Gradient Variational Inference
For strongly concave log-likelihoods, square-root (Cholesky) parametrization of Gaussian variational inference yields exponential convergence guarantees for both the natural-gradient flow and a discrete-time natural-gradient algorithm.