David D. Lewis and William A. Gale. 1994. A sequential algorithm for training text classifiers. In SIGIR’94, pages 3-12.


Tong Luo, Kurt Kramer, Dmitry B. Goldgof, Lawrence O. Hall, Scott Samson, Andrew Remsen, and Thomas Hopkins. 2005. Active Learning to Recognize Multiple Types of Plankton. J. Mach. Learn. Res. 6, pages 589–613.


Burr Settles, Mark Craven, and Soumya Ray. 2007. Multiple-instance active learning. In Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS’07). Curran Associates Inc., Red Hook, pages 1289–1296.


Alex Holub, Pietro Perona, and Michael C. Burl. 2008. Entropy-based active learning for object recognition. In 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, pages 1–8.


Jingbo Zhu, Huizhen Wang, and Eduard Hovy. 2008. Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 1129–1136.


M. Bloodgood and K. Vijay-Shanker. 2009. A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping. In Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL ‘09). Association for Computational Linguistics, USA, 39–47.


Burr Settles. 2010. Active Learning Literature Survey. Computer Sciences Technical Report 1648 University of Wisconsin–Madison.


Ye Zhang, Matthew Lease, and Byron C. Wallace. 2017. Active discriminative text representation learning. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI’17). AAAI Press, pages 3386–3392.


Jeremy Howard and Sebastian Ruder. 2008. Universal Language Model Fine-tuning for Text Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 328–339.


Oscar Reyes, Carlos Morell, and Sebastián Ventura. 2018. Effective Active Learning Strategy for Multi-Label Learning. Neurocomputing 273, pages 494–508.


Michael Altschuler and Michael Bloodgood. 2019. Stopping Active Learning based on Predicted Change of F Measure for Text Classification. In International Conference on Semantic Computing (ICSC 2019).


Daniel Gissin and Shai Shalev-Shwartz. 2019. Discriminative Active Learning. ArXiv abs/1907.06347.


Jordan T. Ash, Chicheng Zhang, Akshay Krishnamurthy, John Langford and Alekh Agarwal. 2020. Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. International Conference on Learning Representations 2020 (ICLR 2020).


Michelle Yuan, Hsuan-Tien Lin, and Jordan Boyd-Graber. 2020. Cold-start Active Learning through Self-supervised Language Modeling. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) Association for Computational Linguistics, pages 7935–7948.


Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020. Active Learning for BERT: An Empirical Study. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7949–7962.


Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert Nowak, Roshan Sumbaly, Matei Zaharia, I. Zeki Yalniz. 2020. Similarity Search for Efficient Active Learning and Search of Rare Concepts. ArXiv abs/2007.00077v2.


Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021. Active Learning by Acquiring Contrastive Examples. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 650–663.


Christopher Schröder, Andreas Niekler and Martin Potthast. 2022. Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2194–2203.