SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors

TMLR Paper9429 Authors

Abstract: Conformal Prediction (CP) provides robust uncertainty guarantees for predictive models, but is typically applied post hoc, which misaligns model training with the conformal goal of producing efficient (i.e, narrow) intervals. We propose SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss. SPACR jointly optimizes efficiency and validity without batch-splitting or a predefined confidence levels during training. As a result, a single SPACR model yields valid prediction intervals at multiple confidence levels during inference, avoiding the costly retraining required by methods like DOICR. Experiments on diverse datasets show that SPACR consistently gives tighter intervals and better coverage-efficiency trade-offs compared to standard CP and DOICR, while significantly reducing computational costs.

Submission Type: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission: Correction of some formatting errors

Assigned Action Editor: ~Qi_CHEN6

Submission Number: 9429