============ Bibliography ============ Active Learning =============== .. [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. .. [ZWH08] 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. .. [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. .. [HHG+11] Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011. `Bayesian Active Learning for Classification and Preference Learning `_. ArXiv, abs/1112.5745. .. [LG13] Xin Li and Yuhong Guo. 2013. Active Learning with Multi-Label SVM Classification. In Proceedings of the Twenty-Third International Joint conference on Artificial Intelligence (IJCAI '13). AAAI Press, 1479–1485. .. [GZ16] Yarin Gal and Zoubin Ghahramani. 2016. `Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning `_. Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1050-1059. .. [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. .. [BLK18] Olivier Bachem, Mario Lucic, and Andreas Krause. 2018. `Scalable k-Means Clustering via Lightweight Coresets `_. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, NY, USA, 1119–1127. .. [RCV18] Oscar Reyes, Carlos Morell, and Sebastián Ventura. 2018. `Effective Active Learning Strategy for Multi-Label Learning `_. Neurocomputing 273, pages 494–508. .. [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). .. [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. .. [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. Query Strategies ================ .. [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. .. [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. .. [CCK+22] Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Culatana, Peter Bailis, Alexander C. Berg, Robert Nowak, Roshan Sumbaly, Matei Zaharia, and I. Zeki Yalniz. 2022. `Similarity Search for Efficient Active Learning and Search of Rare Concepts `_. Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), pages 6402–6410. .. [YDH+22] Ofer Yehuda, Avihu Dekel, Guy Hacohen, Daphna Weinshall. 2022. `Active Learning Through a Covering Lens `_. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), pages 22354–22367. .. [LV24] Pietro Lesci and Andreas Vlachos. 2024. `AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets `_. ArXiv abs/2404.05623. Misc ==== .. [JGP+17] Eric Jang, Shixiang Gu, and Ben Poole. 2017. `Categorical Reparameterization with Gumbel-Softmax. `__ International Conference on Learning Representations 2017 (ICLR 2017). .. [TRE+22] Lewis Tunstall, Nils Reimers, Unso Eun Seo Jo, Luke Bates, Daniel Korat, Moshe Wasserblat, and Oren Pereg. 2022. `Efficient Few-Shot Learning Without Prompts `_. ArXiv, abs/2209.11055.