Conformal inference provides a general distribution-free method to rigorously calibrate the
output of any machine learning algorithm for novelty detection. While this approach has …

Early stopping based on hold-out data is a popular regularization technique designed to
mitigate overfitting and increase the predictive accuracy of neural networks. Models trained …

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 …

ABSTRACT The Generalized Linear Model (GLM) has been widely used in practice to
model counts or other types of non-Gaussian data. This article introduces a framework for …

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 …

Accurate and spatially distributed precipitation data are fundamental to effective water
resource management. In Morocco, as in other arid and semi-arid regions, precipitation …

This paper presents a new conformal method for generating simultaneous forecasting bands
guaranteed to cover the entire path of a new random trajectory with sufficiently high …

Voxel-based multiple testing is widely used in neuroimaging data analysis. Traditional false
discovery rate (FDR) control methods often ignore the spatial dependence among the voxel …

Negative control is a common technique in scientific investigations and broadly refers to the
situation where a null effect (''negative result'') is expected. Motivated by a real proteomic …