Bibliography

LG94

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

LUO05

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.

Set07

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.

HOL08

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.

BV09

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.

Set10

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

ZLW17

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.

HR18

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.

AB19

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).

GS19

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

AZK+20

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).

YLB20

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.

EHG+20

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.

CCK+21

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.

MVB+21

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.

SNP22

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.