This is the official implementation of the paper How To Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
by Jacopo Teneggi, Matt Tivnan, J Webster Stayman, and Jeremias Sulam.
$K$-RCPS is a high-dimensional extension of the Risk Controlling Prediction Sets (RCPS) procedure that provably minimizes the mean interval length.
It is based on $\ell^{\gamma}$: a convex upper-bound to the $01$ loss $\ell^{01}$
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 $\approx 9$%.
@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}
}