Abstract Graph Neural Networks (GNNs) are powerful machine learning prediction models
on graph-structured data. However, GNNs lack rigorous uncertainty estimates, limiting their …

Graph Neural Networks (GNNs) excel in diverse tasks, yet their applications in high-stakes
domains are often hampered by unreliable predictions. Although numerous uncertainty …

In the big data era, subsampling or sub-data selection techniques are often adopted to
extract a fraction of informative individuals from the massive data. Existing subsampling …

Conformal inference is a popular tool for constructing prediction intervals. We consider here
the scenario of post-selection/selective conformal inference, that is, prediction intervals are …

Conformal prediction builds marginally valid prediction intervals which cover the unknown
outcome of a randomly drawn new test point with a prescribed probability. In practice, a …

This paper introduces weighted conformal p-values for model-free selective inference.
Assume we observe units with covariates $ X $ and missing responses $ Y $, the goal is to …

Several uncertainty estimation methods have been recently proposed for machine
translation evaluation. While these methods can provide a useful indication of when not to …

Before deploying outputs from foundation models in high-stakes tasks, it is imperative to
ensure that they align with human values. For instance, in radiology report generation …

Screening classifiers are increasingly used to identify qualified candidates in a variety of
selection processes. In this context, it has been recently shown that if a classifier is …

Most link prediction methods return estimates of the connection probability of missing edges
in a graph. Such output can be used to rank the missing edges, from most to least likely to be …