How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control

This is the official implementation of the paper How To Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control @ ICML 2023

by Jacopo Teneggi, Matt Tivnan, J Webster Stayman, and Jeremias Sulam.

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$K$-RCPS is a high-dimensional extension of the Risk Controlling Prediction Sets (RCPS) procedure that provably minimizes the mean interval length by means of a convex relaxation.

It is based on $\ell^{\gamma}$: a convex upper-bound to the $01$loss$\ell^{01}$

Demo

The demo is included in the demo.ipynb notebook. It showcases how to use the $K$-RCPS calibration procedure on dummy data.

which reduces the mean interval length compared to RCPS on the same data by $\approx 9$%.

Reproducibility

All model checkpoints are available on Zenodo alongside the perturbed images used in the paper. checkpoints.zip and denoising.zip should both be unzipped in the experiments folder.

References

@article{teneggi2023trust,
  title={How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control},
  author={Teneggi, Jacopo and Tivnan, Matt and Stayman, J Webster and Sulam, Jeremias},
  journal={arXiv preprint arXiv:2302.03791},
  year={2023}
}