Split conformal prediction has recently sparked great interest due to its ability to provide
formally guaranteed uncertainty sets or intervals for predictions made by black-box neural …

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 …

Quantifying uncertainty in automatically generated text is important for letting humans check
potential hallucinations and making systems more reliable. Conformal prediction is an …

In this work, we aim to accurately predict the number of hospitalizations during the COVID-
19 pandemic by developing a spatiotemporal prediction model. We propose HOIST, an Ising …

Given a sequence of observable variables $\{(x_1, y_1),\ldots,(x_n, y_n)\} $, the conformal
prediction method estimates a confidence set for $ y_ {n+ 1} $ given $ x_ {n+ 1} $ that is …

Early time classification algorithms aim to label a stream of features without processing the
full input stream, while maintaining accuracy comparable to that achieved by applying the …

Conformal prediction (CP), a distribution-free uncertainty quantification (UQ) framework,
reliably provides valid predictive inference for black-box models. CP constructs prediction …

Inference for prediction errors is critical in time series forecasting pipelines. However,
providing statistically meaningful uncertainty intervals for prediction errors remains relatively …

The last decade in deep learning has brought on increasingly capable systems that are
deployed on a wide variety of applications. In natural language processing, the field has …

Split conformal prediction is a statistical method known for its finite-sample coverage
guarantees, simplicity, and low computational cost. As such, it is suitable for predicting …