Quantifying Epistemic Uncertainty in Diffusion Models

Aditi Gupta, Raphael A Meyer, Yotam Yaniv, Elynn Chen, N. Benjamin Erichson

TL;DR: FLARE is a randomized Fisher–Laplace method that isolates epistemic uncertainty in diffusion models, producing accurate uncertainty estimates for diagnostics and filtering low-confidence samples.

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 projection guided by 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.

Submission Number: 1574