Conformalized Interval Arithmetic with Symmetric Calibration
This work addresses uncertainty quantification for aggregated predictions in decision-making, offering a novel extension of conformal prediction but is incremental in building on existing single-prediction methods.
The paper tackles the problem of extending conformal prediction to estimate sums or averages of multiple unknown labels, providing valid coverage guarantees under permutation invariance. It demonstrates that the proposed method outperforms existing conformalized and non-conformal approaches in tasks like class average estimation and path cost prediction.
Uncertainty quantification is essential in decision-making, especially when joint distributions of random variables are involved. While conformal prediction provides distribution-free prediction sets with valid coverage guarantees, it traditionally focuses on single predictions. This paper introduces novel conformal prediction methods for estimating the sum or average of unknown labels over specific index sets. We develop conformal prediction intervals for single target to the prediction interval for sum of multiple targets. Under permutation invariant assumptions, we prove the validity of our proposed method. We also apply our algorithms on class average estimation and path cost prediction tasks, and we show that our method outperforms existing conformalized approaches as well as non-conformal approaches.