Uniform-in-Time Wasserstein Stability Bounds for (Noisy) Stochastic Gradient Descent

Neural Information Processing Systems 

It also illustrates that ergodicity is an important component for obtaining time-uniform bounds - which might not be achieved for convex or non-convex losses unless additional noise is injected to the iterates.

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