# Label Noise Robustness of Conformal Prediction

arXiv: https://arxiv.org/abs/2209.14295 · HTML: https://mlsanity.com/paper/2209.14295/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2022.

## Built on by (most cited first, 20 of 20)

- [Conformal Prediction for Deep Classifier via Label Ranking](https://mlsanity.com/paper/2310.06430/) (2023)
- [Training-Conditional Coverage Bounds under Covariate Shift](https://mlsanity.com/paper/2405.16594/) (2024)
- [Robust Conformal Prediction Using Privileged Information](https://mlsanity.com/paper/2406.05405/) (2024)
- [Exploring the Noise Robustness of Online Conformal Prediction](https://mlsanity.com/paper/2501.18363/) (2025)
- [Calibrate, Don't Curate: Label-Efficient Estimation from Noisy LLM Judges](https://mlsanity.com/paper/2605.09702/) (2026)
- [Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs](https://mlsanity.com/paper/2506.13593/) (2025)
- [From Conformal Predictions to Confidence Regions](https://mlsanity.com/paper/2405.18601/) (2024)
- [Meta-Analysis with Untrusted Data](https://mlsanity.com/paper/2407.09387/) (2024)
- [Efficient Conformal Prediction for Regression Models under Label Noise](https://mlsanity.com/paper/2509.15120/) (2025)
- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction](https://mlsanity.com/paper/2504.17655/) (2025)
- [Split Conformal Classification with Unsupervised Calibration](https://mlsanity.com/paper/2510.07185/) (2025)
- [Weighted Conformal Prediction Provides Adaptive and Valid Mask-Conditional Coverage for General Missing Data Mechanisms](https://mlsanity.com/paper/2512.14221/) (2025)
- [Partial Causal Structure Learning for Valid Selective Conformal Inference under Interventions](https://mlsanity.com/paper/2603.02204/) (2026)
- [Conformal Robust Set Estimation](https://mlsanity.com/paper/2604.18441/) (2026)
- [Set-Valued Policy Learning](https://mlsanity.com/paper/2605.19830/) (2026)
- [Online Conformal Prediction with Corrupted Feedback](https://mlsanity.com/paper/2605.20515/) (2026)
- [Certified Domain Consistency for Multi-Domain Retrieval: Label-Free Per-Domain Contamination Control with Conformal Risk Guarantees](https://mlsanity.com/paper/2607.14157/) (2026)
- [Confidence Calibration of Deep Learning Systems](https://mlsanity.com/paper/2608.12100/) (2026)
- [Certified Against Which Oracle? Execution Labels Set the Reported Risk of Conformal Abstention for Text-to-SQL](https://mlsanity.com/paper/2609.25938/) (2026)

## Abstract (arXiv)

We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, characterizing when and how it is possible to construct uncertainty sets that correctly cover the unobserved noiseless ground truth labels. We further extend our theory and formulate the requirements for correctly controlling a general loss function, such as the false negative proportion, with noisy labels. Our theory and experiments suggest that conformal prediction and risk-controlling techniques with noisy labels attain conservative risk over the clean ground truth labels whenever the noise is dispersive and increases variability. In other adversarial cases, we can also correct for noise of bounded size in the conformal prediction algorithm in order to ensure achieving the correct risk of the ground truth labels without score or data regularity.