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Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity
The proximal algorithm is a powerful tool to minimize nonlinear and nonsmooth functionals in a general metric space. Motivated by the recent progress in studying the training dynamics of the noisy gradient descent algorithm on two-layer neural networks in the mean-field regime, we provide in this pa
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