Drifting with Gaussian kernels exactly matches score-matching on smoothed distributions via Tweedie's formula, while Laplace kernels approximate this closely in high dimensions.
Assumption 7(Bounded (Feature) Norm).There exists B <∞ independent of D such that for every µ in the above family, ifx∼µthen ∥x∥2 ≤Balmost surely
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A Unified View of Score-Based and Drifting Models
Drifting with Gaussian kernels exactly matches score-matching on smoothed distributions via Tweedie's formula, while Laplace kernels approximate this closely in high dimensions.