A unified framework decomposes Wasserstein gradient flow velocity fields across f-divergences into a shared beta direction and divergence-specific weighting, enabling data-free one-step sampling.
Substituting the RBF kernel instead yields the exact score ( ∇log ˆq= 2 τ mrbf q ); the Laplace mean-shift is an approximation that does not correspond to any standard KDE gradient
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A Unified Framework for Data-Free One-Step Sampling via Wasserstein Gradient Flows
A unified framework decomposes Wasserstein gradient flow velocity fields across f-divergences into a shared beta direction and divergence-specific weighting, enabling data-free one-step sampling.