Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Large language models (LLMs) exhibit a wide range of promising capabilities--from step-by-
step planning to commonsense reasoning--that may provide utility for robots, but remain …

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

The proliferation of open-source Large Language Models (LLMs) from various institutions
has highlighted the urgent need for comprehensive evaluation methods. However, current …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

We extend conformal prediction to control the expected value of any monotone loss function.
The algorithm generalizes split conformal prediction together with its coverage guarantee …

Current approaches for multi-horizon time series forecasting using recurrent neural networks
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …

Black-box machine learning models are now routinely used in high-risk settings, like
medical diagnostics, which demand uncertainty quantification to avoid consequential model …

Standard conformal prediction methods provide a marginal coverage guarantee, which
means that for a random test point, the conformal prediction set contains the true label with a …