Score-based Outlier Generation via Controlling the Radon-Nikodym Derivative
Read the original on arXiv Machine Learning →The paper introduces a measure‑theoretic definition of outliers based on the distribution of log‑likelihood values, ensuring that low‑likelihood events receive higher probability mass with a controllable magnitude. It shows how likelihood reweighting scales the diffusion score via the Radon‑Nikodym derivative, allowing the reverse‑time dynamics of a diffusion model to be modified without retraining. Using the Ornstein‑Uhlenbeck semigroup, the authors propose an exponentially interpolated controller that approximates the true control, enabling controlled generation of low‑likelihood samples that respect the data geometry.
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