Add links to bibliography

webis-de · Jul 9, 2024 · aa19b2b · aa19b2b
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Showing 1 changed file with 23 additions and 23 deletions.
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 ============
 
 .. [LG94] David D. Lewis and William A. Gale. 1994.
-   A sequential algorithm for training text classifiers.
+   `A sequential algorithm for training text classifiers <https://doi.org/10.1007/978-1-4471-2099-5_1>`_.
    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.
+   `Active Learning to Recognize Multiple Types of Plankton <https://www.jmlr.org/papers/v6/luo05a.html>`_.
    J. Mach. Learn. Res. 6, pages 589–613.
 
 .. [Set07] Burr Settles, Mark Craven, and Soumya Ray. 2007.
-   Multiple-instance active learning.
+   `Multiple-instance active learning <https://papers.nips.cc/paper_files/paper/2007/hash/a1519de5b5d44b31a01de013b9b51a80-Abstract.html>`_.
    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.
+   `Entropy-based active learning for object recognition <https://doi.org/10.1109/CVPRW.2008.4563068>`_.
    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.
+   `Multi-Criteria-Based Strategy to Stop Active Learning for Data Annotation <https://aclanthology.org/C08-1142/>`_.
    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.
+   `A method for stopping active learning based on stabilizing predictions and the need for user-adjustable stopping <https://aclanthology.org/W09-1107/>`_.
    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.
+   `Active Learning Literature Survey <http://digital.library.wisc.edu/1793/60660>`_.
    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.
+   `Bayesian Active Learning for Classification and Preference Learning <https://doi.org/10.48550/arXiv.1112.5745>`_.
    ArXiv, abs/1112.5745.
 
 .. [GZ16] Yarin Gal and Zoubin Ghahramani. 2016.
-   Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.
+   `Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning <https://proceedings.mlr.press/v48/gal16.html>`_.
    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.
+   `Active discriminative text representation learning <https://doi.org/10.1609/aaai.v31i1.10962>`_.
    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.
+   `Scalable k-Means Clustering via Lightweight Coresets <https://doi.org/10.1145/3219819.3219973>`_.
    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.
 
 .. [HR18] Jeremy Howard and Sebastian Ruder. 2008.
-   Universal Language Model Fine-tuning for Text Classification.
+   `Universal Language Model Fine-tuning for Text Classification <https://doi.org/10.18653/v1/P18-1031>`_.
    In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, pages 328–339.
 
 .. [RCV18] Oscar Reyes, Carlos Morell, and Sebastián Ventura. 2018.
-   Effective Active Learning Strategy for Multi-Label Learning.
+   `Effective Active Learning Strategy for Multi-Label Learning <https://doi.org/10.1016/j.neucom.2017.08.001>`_.
    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.
+   `Stopping Active Learning based on Predicted Change of F Measure for Text Classification <https://doi.org/10.1109/ICOSC.2019.8665646>`_.
    In International Conference on Semantic Computing (ICSC 2019).
 
 .. [GS19] Daniel Gissin and Shai Shalev-Shwartz. 2019.
-   Discriminative Active Learning.
+   `Discriminative Active Learning <https://doi.org/10.48550/arXiv.1907.06347>`_.
    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.
+   `Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds <https://doi.org/10.48550/arXiv.1906.03671>`_.
    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.
+   `Cold-start Active Learning through Self-supervised Language Modeling <https://doi.org/10.18653/v1/2020.emnlp-main.637>`_.
    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.
+   `Active Learning for BERT: An Empirical Study <https://doi.org/10.18653/v1/2020.emnlp-main.638>`_.
    In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7949–7962.
 
 .. [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.
+   `Similarity Search for Efficient Active Learning and Search of Rare Concepts <https://doi.org/10.48550/arXiv.2007.00077>`_.
    Proceedings of the AAAI Conference on Artificial Intelligence, 36(6), 6402-6410
 
 .. [MVB+21] Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021.
-   Active Learning by Acquiring Contrastive Examples.
+   `Active Learning by Acquiring Contrastive Examples <https://doi.org/10.18653/v1/2021.emnlp-main.51>`_.
    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.
+   `Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers <https://doi.org/10.18653/v1/2022.findings-acl.172>`_.
    In Findings of the Association for Computational Linguistics: ACL 2022, pages 2194–2203.
 
 .. [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.
+   `Efficient Few-Shot Learning Without Prompts <https://doi.org/10.48550/arXiv.2209.11055>`_.
    ArXiv, abs/2209.11055.
 
 .. [LV24] Pietro Lesci and Andreas Vlachos. 2024.
-   AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets.
+   `AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced Datasets <https://aclanthology.org/2024.naacl-long.467>`_.
    ArXiv abs/2404.05623.