# Binary classification with corrupted labels

arXiv: https://arxiv.org/abs/2106.09136 · HTML: https://mlsanity.com/paper/2106.09136/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 12 of 12)

- [Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach](https://mlsanity.com/paper/1609.03683/) (2016)
- [Learning with Symmetric Label Noise: The Importance of Being Unhinged](https://mlsanity.com/paper/1505.07634/) (2015)
- [Classification with Asymmetric Label Noise: Consistency and Maximal Denoising](https://mlsanity.com/paper/1303.1208/) (2013)
- [Learning with Bounded Instance- and Label-dependent Label Noise](https://mlsanity.com/paper/1709.03768/) (2017)
- [Making Risk Minimization Tolerant to Label Noise](https://mlsanity.com/paper/1403.3610/) (2014)
- [Loss factorization, weakly supervised learning and label noise robustness](https://mlsanity.com/paper/1602.02450/) (2016)
- [Concentration in unbounded metric spaces and algorithmic stability](https://mlsanity.com/paper/1309.1007/) (2013)
- [Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise](https://mlsanity.com/paper/1906.04542/) (2019)
- [Classification with unknown class-conditional label noise on non-compact feature spaces](https://mlsanity.com/paper/1902.05627/) (2019)
- [Learning from Binary Labels with Instance-Dependent Corruption](https://mlsanity.com/paper/1605.00751/) (2016)
- [Learning to Combat Noisy Labels via Classification Margins](https://mlsanity.com/paper/2102.00751/) (2021)
- [A Mathematical Programming approach to Binary Supervised Classification with Label Noise](https://mlsanity.com/paper/2004.10170/) (2020)

## Built on by (most cited first, 4 of 4)

- [Adaptive conformal classification with noisy labels](https://mlsanity.com/paper/2309.05092/) (2023)
- [Interpolation can hurt robust generalization even when there is no noise](https://mlsanity.com/paper/2108.02883/) (2021)
- [Binary Classification with Instance and Label Dependent Label Noise](https://mlsanity.com/paper/2306.03402/) (2023)
- [Leveraging Noisy Manual Labels as Useful Information: An Information Fusion Approach for Enhanced Variable Selection in Penalized Logistic Regression](https://mlsanity.com/paper/2504.16585/) (2025)

## Abstract (arXiv)

In a binary classification problem where the goal is to fit an accurate predictor, the presence of corrupted labels in the training data set may create an additional challenge. However, in settings where likelihood maximization is poorly behaved-for example, if positive and negative labels are perfectly separable-then a small fraction of corrupted labels can improve performance by ensuring robustness. In this work, we establish that in such settings, corruption acts as a form of regularization, and we compute precise upper bounds on estimation error in the presence of corruptions. Our results suggest that the presence of corrupted data points is beneficial only up to a small fraction of the total sample, scaling with the square root of the sample size.