Conformal prediction (CP) enables machine learning models to output prediction sets with
guaranteed coverage rate, assuming exchangeable data. Unfortunately, the exchangeability …

Conformal prediction can be applied on top of any machine learning predictive regression
model, thus turning it into a conformal regressor. Given a significance level ϵ, conformal …

We develop fast distribution-free conformal prediction algorithms for obtaining multivalid
coverage on exchangeable data in the batch setting. Multivalid coverage guarantees are …

TorchCP is a Python toolbox for conformal prediction research on deep learning models. It
contains various implementations for posthoc and training methods for classification and …

Deep Learning predictions with measurable confidence are increasingly desirable for real-
world problems, especially in high-risk settings. The Conformal Prediction (CP) framework is …

Conformal predictors are an important class of algorithms that allow predictions to be made
with a user-defined confidence level. They are able to do this by outputting prediction sets …

Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that
constructs prediction sets guaranteed to contain the true answer with a user-specified …

In this paper, we focus on the problem of conformal prediction with conditional guarantees.
Prior work has shown that it is impossible to construct nontrivial prediction sets with full …

Weighted conformal prediction (WCP), a recently proposed framework, provides uncertainty
quantification with the flexibility to accommodate different covariate distributions between …

Conformal prediction methodologies have significantly advanced the quantification of
uncertainties in predictive models. Yet, the construction of confidence regions for model …