Conformalized Deep Splines for Optimal and Efficient Prediction Sets

• URL: http://arxiv.org/abs/2311.00774v1
• Date: Wed, 1 Nov 2023 18:37:07 GMT
• Title: Conformalized Deep Splines for Optimal and Efficient Prediction Sets
• Authors: Nathaniel Diamant, Ehsan Hajiramezanali, Tommaso Biancalani, Gabriele Scalia
• Abstract summary: We present a new conformal regression method, Spline Prediction Intervals via Conformal Estimation (SPICE) We prove universal approximation and optimality results for SPICE, which are empirically validated by our experiments. Results on benchmark datasets demonstrate SPICE-ND models achieve the smallest average prediction set sizes.
• Score: 4.676979941493237
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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