Conformalized Kernel Ridge Regression

TL;DR

This paper introduces a conformal method for Kernel Ridge Regression that produces valid, non-parametric confidence regions, offering a practical alternative to Bayesian methods for uncertainty quantification.

Contribution

It presents a computationally efficient conformal procedure for KRR and compares its performance with Bayesian confidence sets in predictive confidence region construction.

Findings

Conformalized KRR achieves specified coverage rates.

Conformal regions are competitive with Bayesian confidence sets.

Method enhances uncertainty quantification in predictive models.

Abstract

General predictive models do not provide a measure of confidence in predictions without Bayesian assumptions. A way to circumvent potential restrictions is to use conformal methods for constructing non-parametric confidence regions, that offer guarantees regarding validity. In this paper we provide a detailed description of a computationally efficient conformal procedure for Kernel Ridge Regression (KRR), and conduct a comparative numerical study to see how well conformal regions perform against the Bayesian confidence sets. The results suggest that conformalized KRR can yield predictive confidence regions with specified coverage rate, which is essential in constructing anomaly detection systems based on predictive models.