Conservative drifting with kernel density estimators achieves provable convergence rates for one-step generative modeling, with the convergence speed depending on dimension and a tunable parameter that trades off between different error sources.
This paper analyzes drifting methods for generative modeling, proposing a conservative approach using kernel density estimators that guarantees gradient-field properties. The authors prove finite-particle convergence rates showing how quickly the method converges as sample size increases, with explicit tracking of how bandwidth and dimension affect performance.