# Efficiency of conformalized ridge regression

arXiv: https://arxiv.org/abs/1404.2083 · HTML: https://mlsanity.com/paper/1404.2083/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2014.

## Built on by (most cited first, 13 of 13)

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- [Document Summarization with Conformal Importance Guarantees](https://mlsanity.com/paper/2509.20461/) (2025)
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- [Conformal Prediction Under Generalized Covariate Shift with Posterior Drift](https://mlsanity.com/paper/2502.17744/) (2025)
- [Beyond Conformal Predictors: Adaptive Conformal Inference with Confidence Predictors](https://mlsanity.com/paper/2409.15548/) (2024)
- [Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence](https://mlsanity.com/paper/2507.20272/) (2025)
- [On some practical challenges of conformal prediction](https://mlsanity.com/paper/2510.10324/) (2025)
- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [Conformalized Percentile Interval: Finite Sample Validity and Improved Conditional Performance](https://mlsanity.com/paper/2605.03233/) (2026)
- [Conformal Regression in Calorie Prediction for Team Jumbo-Visma](https://mlsanity.com/paper/2304.03778/) (2023)
- [Leverage-Weighted Conformal Prediction](https://mlsanity.com/paper/2602.12693/) (2026)
- [Robust Bayes-Assisted Conformal Prediction](https://mlsanity.com/paper/2607.04236/) (2026)

## Abstract (arXiv)

Conformal prediction is a method of producing prediction sets that can be applied on top of a wide range of prediction algorithms. The method has a guaranteed coverage probability under the standard IID assumption regardless of whether the assumptions (often considerably more restrictive) of the underlying algorithm are satisfied. However, for the method to be really useful it is desirable that in the case where the assumptions of the underlying algorithm are satisfied, the conformal predictor loses little in efficiency as compared with the underlying algorithm (whereas being a conformal predictor, it has the stronger guarantee of validity). In this paper we explore the degree to which this additional requirement of efficiency is satisfied in the case of Bayesian ridge regression; we find that asymptotically conformal prediction sets differ little from ridge regression prediction intervals when the standard Bayesian assumptions are satisfied.