# Conformalized Kernel Ridge Regression

arXiv: https://arxiv.org/abs/1609.05959 · HTML: https://mlsanity.com/paper/1609.05959/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2016.

## Built on by (most cited first, 6 of 6)

- [Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees](https://mlsanity.com/paper/2401.07733/) (2024)
- [Meta-Analysis with Untrusted Data](https://mlsanity.com/paper/2407.09387/) (2024)
- [Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference](https://mlsanity.com/paper/2408.01582/) (2024)
- [StreaMRAK a Streaming Multi-Resolution Adaptive Kernel Algorithm](https://mlsanity.com/paper/2108.10411/) (2021)
- [Conformalized Polynomial Chaos Expansion for Uncertainty-aware Surrogate Modeling](https://mlsanity.com/paper/2510.22375/) (2025)
- [Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA](https://mlsanity.com/paper/2607.21823/) (2026)

## Abstract (arXiv)

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.