# Online Active Learning of Reject Option Classifiers

arXiv: https://arxiv.org/abs/1906.06166 · HTML: https://mlsanity.com/paper/1906.06166/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2019.

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

- [Sample Efficient Learning of Predictors that Complement Humans](https://mlsanity.com/paper/2207.09584/) (2022)
- [Active learning for data streams: a survey](https://mlsanity.com/paper/2302.08893/) (2023)
- [Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing](https://mlsanity.com/paper/2304.04906/) (2023)
- [Promises and Pitfalls of Threshold-based Auto-labeling](https://mlsanity.com/paper/2211.12620/) (2022)
- [Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation](https://mlsanity.com/paper/2312.01835/) (2023)
- [Classification with Reject Option: Distribution-free Error Guarantees via Conformal Prediction](https://mlsanity.com/paper/2506.21802/) (2025)
- [RISAN: Robust Instance Specific Abstention Network](https://mlsanity.com/paper/2107.03090/) (2021)
- [Graceful Degradation and Related Fields](https://mlsanity.com/paper/2106.11119/) (2021)
- [Towards Calibrated Losses for Adversarial Robust Reject Option Classification](https://mlsanity.com/paper/2410.10736/) (2024)

## Abstract (arXiv)

Active learning is an important technique to reduce the number of labeled examples in supervised learning. Active learning for binary classification has been well addressed in machine learning. However, active learning of the reject option classifier remains unaddressed. In this paper, we propose novel algorithms for active learning of reject option classifiers. We develop an active learning algorithm using double ramp loss function. We provide mistake bounds for this algorithm. We also propose a new loss function called double sigmoid loss function for reject option and corresponding active learning algorithm. We offer a convergence guarantee for this algorithm. We provide extensive experimental results to show the effectiveness of the proposed algorithms. The proposed algorithms efficiently reduce the number of label examples required.