How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
This work addresses reliability issues for diffusion models in critical applications like medical imaging, offering incremental improvements in risk control methods.
The authors tackled the problem of providing uncertainty guarantees for diffusion models in image-to-image tasks by developing a convex optimization approach for conformal risk control, achieving state-of-the-art performance with minimal mean interval length on real-world denoising problems like face images and CT scans.
Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks. While they provide high-quality and diverse samples from empirical distributions, important questions remain on the reliability and trustworthiness of these sampling procedures for their responsible use in critical scenarios. Conformal prediction is a modern tool to construct finite-sample, distribution-free uncertainty guarantees for any black-box predictor. In this work, we focus on image-to-image regression tasks and we present a generalization of the Risk-Controlling Prediction Sets (RCPS) procedure, that we term $K$-RCPS, which allows to $(i)$ provide entrywise calibrated intervals for future samples of any diffusion model, and $(ii)$ control a certain notion of risk with respect to a ground truth image with minimal mean interval length. Differently from existing conformal risk control procedures, ours relies on a novel convex optimization approach that allows for multidimensional risk control while provably minimizing the mean interval length. We illustrate our approach on two real-world image denoising problems: on natural images of faces as well as on computed tomography (CT) scans of the abdomen, demonstrating state of the art performance.