Conformal Validity Guarantees Exist for Any Data Distribution

1 code implementation • 10 May 2024 • Drew Prinster, Samuel Stanton, Anqi Liu, Suchi Saria

As machine learning (ML) gains widespread adoption, practitioners are increasingly seeking means to quantify and control the risk these systems incur.

JAWS: Auditing Predictive Uncertainty Under Covariate Shift

1 code implementation • 21 Jul 2022 • Drew Prinster, Anqi Liu, Suchi Saria

We propose \textbf{JAWS}, a series of wrapper methods for distribution-free uncertainty quantification tasks under covariate shift, centered on the core method \textbf{JAW}, the \textbf{JA}ckknife+ \textbf{W}eighted with data-dependent likelihood-ratio weights.

Uncertainty Quantification