An Analysis of Active Learning Algorithms using Real-World Crowd-sourced Text Annotations
Active learning algorithms automatically identify the most informative samples from large amounts of unlabeled data and tremendously reduce human annotation effort in inducing a machine learning model. In a conventional active learning setup, the labeling oracles are assumed to be infallible, that is, they always provide correct answers (in terms of class labels) to the queried unlabeled instances, which cannot be guaranteed in real-world applications. To this end, a body of research has focused on the development of active learning algorithms in the presence of imperfect / noisy oracles. Existing research on active learning with noisy oracles typically simulate the oracles using machine ...
| Authors | Varun Totakura, Ankita Singh, Yushun Dong et al. |
|---|---|
| Primary category | cs.LG |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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