# Bayesian Active Learning With Abstention Feedbacks

arXiv: https://arxiv.org/abs/1906.02179 · HTML: https://mlsanity.com/paper/1906.02179/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Nearest Neighbor Classifier with Margin Penalty for Active Learning](https://mlsanity.com/paper/2203.09174/) (2022)
- [Active learning with biased non-response to label requests](https://mlsanity.com/paper/2312.08150/) (2023)

## Abstract (arXiv)

We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a (1-frac1e) constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.