Accuracy alone does not suffice for reliable, consequential decision-making; we also need uncertainty.

Distribution-free UQ gives finite-sample statistical guarantees for any predictive model, no matter how bad/misspecified, and any data distribution, even if unknown.

DF techniques such as conformal prediction represent a new, principled approach to UQ for complex prediction systems, such as deep learning.

This workshop will bridge applied machine learning and distribution-free uncertainty quantification, catalyzing work at this interface.

This workshop is an inclusive, beginner-friendly introduction to distribution-free UQ. We encourage everybody to submit a paper. Spotlight talks and posters presentations will be selected from the submissions.

What is distribution-free uncertainty quantification?

Distribution-free methods make minimal assumptions about the data distribution or model, yet still provide uncertainty quantification. Examples of DF methods include conformal prediction, tolerance regions, risk-controlling prediction sets, calibration by binning, and more. We take a broad outlook on DF methods, so any assumption-light uncertainty quantification approaches are welcome.