Score-Based Outlier Generation without Retraining via Likelihood Reweighting

Outlier generation plays a fundamental role in stress testing, risk analysis, and trustworthy machine learning. Existing methods typically require specialized architectures or training objectives designed specifically for this task. Instead, we show that a score-based diffusion model trained only on regular data can be turned into a controllable outlier generator without retraining or fine-tuning. We adopt recent insights that formulate outlier generation as reweighting the one-dimensional distribution of log-likelihood values induced by the pretrained model, and introduce exponential tilting as a tractable realization of this idea. This yields an explicit scalar modification of the diffusion score that steers sampling toward lower-likelihood regions. We establish a quantitative relationship between the magnitude of the exponential tilting parameter and the resulting outlier strength, and show that the controlled sampling dynamics retain convergence toward the data manifold. Across nine real-world multivariate time-series datasets, our method ranks first or second in all main benchmark comparisons against specialized outlier-generation methods, while preserving marginal, temporal, and cross-feature structure. These results demonstrate that effective outlier generation can be achieved through likelihood reweighting alone, without training a specialized generative model.