Kernel Density Estimator (KDE)

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models

This paper proposes and analyzes a conservative drifting method for one-step generative modeling, replacing displacement-based velocity with a kernel density estimator (KDE)-gradient velocity. The authors prove continuous-time finite-particle convergence bounds and derive explicit rates depending on bandwidth and dimensionality.