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Score-based diffusion models are increasingly considered in settings where the underlying data distribution may differ from the training distribution, yet existing theoretical guarantees largely focus on the no-shift setting. In this work, we study robust score estimation under Wasserstein perturbations of a reference...
Theoretical analysis demonstrates that the proposed estimator is unbiased, attains finite variance, and satisfies a central limit theorem, and the results demonstrate that in large-scale applications, the unbiased algorithm can be 2–3 orders of magnitude more efficient than the “gold-standard” randomized Hamiltonian Mo...
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