We train models end-to-end with risk control for the broad family of optimized certainty equivalent (OCE) risks.

We introduce a lightweight, post-hoc routing framework, with provable guarantees, that safely delegates between language models with competing objectives.

We design a new conformal prediction method with theoretical guarantees that produces 'confidence masks' for uncertainty quantification of image restoration tasks such as image super-resolution.