There are relatively few works dealing with conformal prediction for multi-task learning
issues, and this is particularly true for multi-target regression. This paper focuses on the …

Decision-making under uncertainty is hugely important for any decisions sensitive to
perturbations in observed data. One method of incorporating uncertainty into making optimal …

Quantifying the uncertainty of a predictive model output is of essential importance in learning
scenarios involving critical applications. As the learning task becomes more complex, so …

Multi-task learning is a domain that is still not fully studied in the conformal prediction
framework, and this is particularly true for multi-target regression. Our work uses inductive …

Accurate uncertainty measurement is a key step to building robust and reliable machine
learning systems. Conformal prediction is a distribution-free uncertainty quantification …

The estimation of the amount of uncertainty featured by predictive machine learning models
has acquired a great momentum in recent years. Uncertainty estimation provides the user …

Though machine learning-based prediction techniques have been widely applied in
industrial production, how to quantify their uncertainty remains a major challenge. Conformal …

It is common in machine learning to estimate a response y given covariate information x.
However, these predictions alone do not quantify any uncertainty associated with said …

We propose conformal hyperrectangular prediction regions for multi‐target regression. We
propose split conformal prediction algorithms for both point and quantile regression to form …

The estimation of the amount of uncertainty featured by predictive machine learning models
has acquired a great momentum in recent years. Uncertainty estimation provides the user …