# Adversarial Label Learning

arXiv: https://arxiv.org/abs/1805.08877 · HTML: https://mlsanity.com/paper/1805.08877/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 9 of 9)

- [A Survey on Programmatic Weak Supervision](https://mlsanity.com/paper/2202.05433/) (2022)
- [Training Subset Selection for Weak Supervision](https://mlsanity.com/paper/2206.02914/) (2022)
- [Weak Supervision Performance Evaluation via Partial Identification](https://mlsanity.com/paper/2312.04601/) (2023)
- [Data Consistency for Weakly Supervised Learning](https://mlsanity.com/paper/2202.03987/) (2022)
- [Robust Knowledge Distillation from RNN-T Models With Noisy Training Labels Using Full-Sum Loss](https://mlsanity.com/paper/2303.05958/) (2023)
- [Label Augmentation with Reinforced Labeling for Weak Supervision](https://mlsanity.com/paper/2204.06436/) (2022)
- [An Adaptive Method for Weak Supervision with Drifting Data](https://mlsanity.com/paper/2306.01658/) (2023)
- [Reliable Programmatic Weak Supervision with Confidence Intervals for Label Probabilities](https://mlsanity.com/paper/2508.03896/) (2025)
- [Regularized Data Programming with Automated Bayesian Prior Selection](https://mlsanity.com/paper/2210.08677/) (2022)

## Abstract (arXiv)

We consider the task of training classifiers without labels. We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifier's error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. Experiments on three real datasets show that our method can train without labels and outperforms other approaches for weakly supervised learning.