Conformalized Deep Splines for Optimal and Efficient Prediction Sets
Abstract
Uncertainty estimation is critical in high-stakes machine learning applications. One effective way to estimate uncertainty is conformal prediction, which can provide predictive inference with statistical coverage guarantees. We present a new conformal regression method, Spline Prediction Intervals via Conformal Estimation (SPICE), that estimates the conditional density using neural-network-parameterized splines. We prove universal approximation and optimality results for SPICE, which are empirically validated by our experiments. SPICE is compatible with two different efficient-to-compute conformal scores, one oracle-optimal for marginal coverage (SPICE-ND) and the other asymptotically optimal for conditional coverage (SPICE-HPD). Results on benchmark datasets demonstrate SPICE-ND models achieve the smallest average prediction set sizes, including average size reductions of nearly 50% for some datasets compared to the next best baseline. SPICE-HPD models achieve the best conditional coverage compared to baselines. The SPICE implementation is made available.
Cite
@article{arxiv.2311.00774,
title = {Conformalized Deep Splines for Optimal and Efficient Prediction Sets},
author = {Nathaniel Diamant and Ehsan Hajiramezanali and Tommaso Biancalani and Gabriele Scalia},
journal= {arXiv preprint arXiv:2311.00774},
year = {2023}
}