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This work studies robust score estimation under Wasserstein perturbations of a reference distribution of Ornstein--Uhlenbeck diffusion and characterize how finite data, intrinsic dimension, and distribution shift affect the robustness of score-based diffusion models.
The ability of AI systems to improve their behavior during deployment is becoming increasingly important. As inference moves beyond the static execution of a fixed trained model, a growing body of work studies how models can refine their behavior on the fly by exploiting test-time information and additional computation...
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