Zero-Shot Uncertainty Quantification using Diffusion Probabilistic Models

TL;DR

This paper investigates the use of diffusion probabilistic models for zero-shot uncertainty quantification in regression tasks, demonstrating ensemble methods improve accuracy and provide confidence estimates without specialized training.

Contribution

It introduces ensemble-based zero-shot uncertainty quantification using diffusion models for regression, with extensive experimental validation across multiple data types.

Findings

Ensemble methods improve prediction accuracy in diffusion regression.

Auto-regressive predictions benefit more from ensembles than point-wise.

Ensemble variance correlates with prediction error, aiding confidence monitoring.

Abstract

The success of diffusion probabilistic models in generative tasks, such as text-to-image generation, has motivated the exploration of their application to regression problems commonly encountered in scientific computing and various other domains. In this context, the use of diffusion regression models for ensemble prediction is becoming a practice with increasing popularity. Under such background, we conducted a study to quantitatively evaluate the effectiveness of ensemble methods on solving different regression problems using diffusion models. We consider the ensemble prediction of a diffusion model as a means for zero-shot uncertainty quantification, since the diffusion models in our study are not trained with a loss function containing any uncertainty estimation. Through extensive experiments on 1D and 2D data, we demonstrate that ensemble methods consistently improve model prediction accuracy across various regression tasks. Notably, we observed a larger accuracy gain in auto-regressive prediction compared with point-wise prediction, and that enhancements take place in both the mean-square error and the physics-informed loss. Additionally, we reveal a statistical correlation between ensemble prediction error and ensemble variance, offering insights into balancing computational complexity with prediction accuracy and monitoring prediction confidence in practical applications where the ground truth is unknown. Our study provides a comprehensive view of the utility of diffusion ensembles, serving as a useful reference for practitioners employing diffusion models in regression problem-solving.