Finite-width shallow networks remain within poly(d) m^{-min(1,c/6)} of their mean-field limit uniformly in time when mean-field excess loss decays as t^{-c} under standard regularity and an integral condition on the loss.
Free energy Wasserstein gradient flow and their particle counter- parts: toy model, (degenerate) PL inequalities and exit times.arXiv e-prints, page arXiv:2510.16506, October 2025
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Mean-field underdamped Langevin dynamics achieves Nesterov acceleration for Wasserstein minimization of displacement-convex free energies by extending a linear-case result to the nonlinear setting.
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Uniform-in-Time Weak Propagation-of-Chaos in Shallow Neural Networks
Finite-width shallow networks remain within poly(d) m^{-min(1,c/6)} of their mean-field limit uniformly in time when mean-field excess loss decays as t^{-c} under standard regularity and an integral condition on the loss.
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Nesterov acceleration for the Wasserstein minimization of displacement-convex free energies
Mean-field underdamped Langevin dynamics achieves Nesterov acceleration for Wasserstein minimization of displacement-convex free energies by extending a linear-case result to the nonlinear setting.