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Daniel Bensimon

dbensimon@uwaterloo.ca
Master's Research - McGill University
Supervisor
Archer Yang
Co-supervisor
Eric Kolaczyk
Research Topics
Machine Learning Theory

Publications

Isotonic Conformal Prediction
Daniel Bensimon
Sean Xiang Yu
Eric D. Kolaczyk
Archer Y. Yang
A point prediction that is well calibrated on average can still be systematically biased conditional on its own value, undermining its use i… (see more)n downstream decision-making. We consider two objectives for reliable uncertainty quantification: self-calibration, requiring a point prediction to be unbiased conditional on its own value, and prediction-conditional validity, requiring a prediction interval to attain nominal coverage conditional on the prediction. Self-Calibrating Conformal Prediction (SC-CP) attains both objectives exactly in finite samples, but requires refitting its calibrator for every candidate outcome, which is computationally prohibitive for continuous outcomes. We propose Isotonic Conformal Prediction (ICP), a framework that decouples calibration from prediction-set construction by fitting a single isotonic recalibration map and constructing prediction intervals within strata of similar recalibrated predictions. Within this framework we develop two procedures. Split Isotonic Conformal Prediction (SICP) attains prediction-conditional validity in finite samples and self-calibration asymptotically, at the computational cost of split conformal prediction. Transductive Isotonic Conformal Prediction (TICP) attains both objectives exactly in finite samples through a per-test-point inner loop that avoids refitting the isotonic calibrator. On synthetic heteroscedastic regression problems and a real-world healthcare-utilization dataset, both procedures match the coverage of SC-CP at substantially lower computational cost.
2026-07-17
arXiv (preprint)
doi.org
arxiv.org