Quantifying Epistemic Uncertainty in Diffusion Models

• URL: http://arxiv.org/abs/2602.09170v1
• Date: Mon, 09 Feb 2026 20:22:33 GMT
• Title: Quantifying Epistemic Uncertainty in Diffusion Models
• Authors: Aditi Gupta, Raphael A. Meyer, Yotam Yaniv, Elynn Chen, N. Benjamin Erichson,
• Abstract summary: Empirically, FLARE improves uncertainty estimation in synthetic time-series generation tasks.<n>We provide analytic and empirical evidence that last-layer Laplace approximations are insufficient for this task.
• Score: 7.745621732213343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models.Existing methods are often unreliable because they mix epistemic and aleatoric uncertainty. We introduce a method based on Fisher information that explicitly isolates epistemic variance, producing more reliable plausibility scores for generated data. To make this approach scalable, we propose FLARE (Fisher-Laplace Randomized Estimator), which approximates the Fisher information using a uniformly random subset of model parameters. Empirically, FLARE improves uncertainty estimation in synthetic time-series generation tasks, achieving more accurate and reliable filtering than other methods. Theoretically, we bound the convergence rate of our randomized approximation and provide analytic and empirical evidence that last-layer Laplace approximations are insufficient for this task.

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