Improving Backward Conformal Prediction via Non-Conformity Score Transformation
A new method, ST-BCP, introduces a data-dependent transformation of non-conformity scores to address the coverage gap in Backward Conformal Prediction (BCP). The approach is theoretically justified and, in experiments on common benchmarks, reduces the average coverage gap from 4.20% to 1.12%.
Why it matters: This work advances uncertainty quantification in machine learning by making prediction sets more reliable under size constraints.
Full story at: arXiv Statistical ML ↗