Quantifying uncertainty in machine learning is important in new research areas with scarce
high-quality data. In this work, we develop an explainable uncertainty quantification method …

Conformal prediction is a theoretically grounded framework for constructing predictive
intervals. We study conformal prediction with missing values in the covariates–a setting that …

Safe deployment of deep neural networks in high-stake real-world applications require
theoretically sound uncertainty quantification. Conformal prediction (CP) is a principled …

Conventional frequentist learning is known to yield poorly calibrated models that fail to
reliably quantify the uncertainty of their decisions. Bayesian learning can improve …

Abstract We develop Temporal Quantile Adjustment (TQA), a general method to construct
efficient and valid prediction intervals (PIs) for regression on cross-sectional time series …

Despite attractive theoretical guarantees and practical successes, Predictive Interval (PI)
given by Conformal Prediction (CP) may not reflect the uncertainty of a given model. This …

Conformal prediction (CP) is a wrapper around traditional machine learning models, giving
coverage guarantees under the sole assumption of exchangeability; in classification …

Cross-sectional prediction is common in many domains such as healthcare, including
forecasting tasks using electronic health records, where different patients form a cross …

Conformal regression provides prediction intervals with global coverage guarantees, but
often fails to capture local error distributions, leading to non-homogeneous coverage. We …

Network traffic prediction techniques have attracted much attention since they are valuable
for network congestion control and user experience improvement. While existing prediction …