Investigating Data Usage for Inductive Conformal Predictors

Hiring-manager view

This paper is a direct uncertainty-quantification signal: it studies the data and calibration choices behind conformal prediction rather than treating uncertainty estimates as a black box.

Scientific problem

Inductive conformal predictors rely on data splits and calibration sets to produce uncertainty-aware prediction sets. The practical question is how data usage decisions affect validity, efficiency, and downstream model behavior.

Method

• Investigated how data allocation choices influence inductive conformal prediction.
• Focused on calibration behavior, data efficiency, and prediction-set quality.
• Connected empirical model behavior to uncertainty guarantees relevant to safety-sensitive ML systems.

Evaluation signal

The evaluation centers on how calibration and data usage choices change uncertainty quality and reliability, especially when model outputs must support downstream decisions.