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Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation, aiming to improve over random sampling.
Query by committee
H. S. Seung, M. Opper, and H. Sompolinsky. 1992 · 1992
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A sequential algorithm for training text classifiers
David D. Lewis and William A. Gale. 1994 · 1994
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Active learning with statistical models
David A. Cohn, Zoubin Ghahramani, and Michael I. Jordan. 1996 · 1996
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Employing em and pool-based active learning for text classification
Andrew McCallum and Kamal Nigam. 1998 · 1998
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Toward optimal active learning through monte carlo estimation of error reduction
Nicholas Roy and Andrew Mccallum. 2001 · 2001
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Incorporating diversity in active learning with support vector machines
Klaus Brinker. 2003 · 2003
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Active learning and the total cost of annotation
Jason Baldridge and Miles Osborne. 2004 · 2004
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Using cluster-based sampling to select initial training set for active learning in text classification
Jaeho Kang, Kwang Ryel Ryu, and Hyuk chul Kwon. 2004 · 2004
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Reducing labeling effort for structured prediction tasks
Aron Culotta and Andrew Mccallum. 2005 · 2005
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Investigating the effects of selective sampling on the annotation task
Ben Hachey, Beatrice Alex, and Markus Becker. 2005 · 2005
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Active annotation
Andreas Vlachos. 2006 · 2006
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Multiple-instance active learning
Burr Settles, Mark Craven, and Soumya Ray. 2007 · 2007
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Proactive learning: Cost-sensitive active learning with multiple imperfect oracles
Pinar Donmez and Jaime G. Carbonell. 2008 · 2008
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A survey of active learning for text classification using deep neural networks
Christopher Schröder and Andreas Niekler. 2020 · 2008
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An analysis of active learning strategies for sequence labeling tasks
Burr Settles and Mark Craven. 2008 · 2008
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A stopping criterion for active learning
Andreas Vlachos. 2008 · 2008
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Jingbo Zhu, Huizhen Wang, Tianshun Yao, and Benjamin K Tsou. 2008 · 2008
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How well does active learning actually
Jason Baldridge and Alexis Palmer. 2009 · 2009
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Mine the easy, classify the hard: A semi-supervised approach to automatic sentiment classification
Sajib Dasgupta and Vincent Ng. 2009 · 2009
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Active learning for statistical phrase-based machine translation
Gholamreza Haffari, Maxim Roy, and Anoop Sarkar. 2009 · 2009
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Active learning literature survey
Burr Settles. 2009 · 2009
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Semi-supervised active learning for sequence labeling
Katrin Tomanek and Udo Hahn. 2009 · 2009
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On proper unit selection in active learning: Co-selection effects for named entity recognition
Katrin Tomanek, Florian Laws, Udo Hahn, and Hinrich Schütze. 2009 · 2009
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Active semi-supervised learning for improving word alignment
Vamshi Ambati, Stephan Vogel, and Jaime Carbonell. 2010 · 2010
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Active learning with clustering
Zalán Bodó, Zsolt Minier, and Lehel Csató. 2011 · 2010
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Off to a good start: Using clustering to select the initial training set in active learning
Rong Hu, Brian Mac Namee, and Sarah Jane Delany. 2010 · 2010
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A comparison of models for cost-sensitive active learning
Katrin Tomanek and Udo Hahn. 2010 · 2010
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On achieving and evaluating language-independence in nlp
Emily M. Bender. 2011 · 2011
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel. 2011 · 2011
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Active learning for Post-Editing based incrementally retrained MT
Aswarth Abhilash Dara, Josef van Genabith, Qun Liu, John Judge, and Antonio Toral. 2014 · 2014
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Investigating active learning for short-answer scoring
Andrea Horbach and Alexis Palmer. 2016 · 2016
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Selecting syntactic, non-redundant segments in active learning for machine translation
Akiva Miura, Graham Neubig, Michael Paul, and Satoshi Nakamura. 2016 · 2016
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Learning algorithms for active learning
Philip Bachman, Alessandro Sordoni, and Adam Trischler. 2017 · 2017
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Deep Bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017 · 2017
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Deep active learning over the long tail
Yonatan Geifman and Ran El-Yaniv. 2017 · 2017
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Learning active learning from data
Ksenia Konyushkova, Raphael Sznitman, and Pascal Fua. 2017 · 2017
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Active learning for Large-Scale entity resolution
Kun Qian, Lucian Popa, and Prithviraj Sen. 2017 · 2017
Cited alongside, same era.
Deep active learning for named entity recognition
Yanyao Shen, Hyokun Yun, Zachary Lipton, Yakov Kronrod, and Animashree Anandkumar. 2017 · 2017
Cited alongside, same era.
Adversarial active learning for deep networks: a margin based approach
Melanie Ducoffe and Frédéric Precioso. 2018 · 2018
Cited alongside, same era.
Learning how to actively learn: A deep imitation learning approach
Ming Liu, Wray Buntine, and Gholamreza Haffari. 2018 · 2018
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2018 · 2018
Cited alongside, same era.
Deep Bayesian active learning for natural language processing: Results of a large-scale empirical study
Active learning for argument strength estimation
Nataliia Kees, Michael Fromm, Evgeniy Faerman, and Thomas Seidl. 2021 · 2021
Later among the works it cites.
Dynabench: Rethinking benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams. 2021 · 2021
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Test distribution-aware active learning: A principled approach against distribution shift and outliers
Andreas Kirsch, Tom Rainforth, and Yarin Gal. 2021 · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. 2021 · 2021
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Mind the gap: Assessing temporal generalization in neural language models
Angeliki Lazaridou, Adhiguna Kuncoro, Elena Gribovskaya, Devang Agrawal, Adam Liska, Tayfun Terzi, Mai Gimenez, Cyprien de Masson d’Autume, Tomáš Kočiský, Sebastian Ruder, Dani Yogatama, Kris Cao, Susannah Young, and Phil Blunsom. 2021 · 2021
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Aditya Siddhant and Zachary C. Lipton. 2018 · 2018
Cited alongside, same era.
Deep visual domain adaptation: A survey
Mei Wang and Weihong Deng. 2018 · 2018
Cited alongside, same era.
Show your work: Improved reporting of experimental results
Jesse Dodge, Suchin Gururangan, Dallas Card, Roy Schwartz, and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
Practical, efficient, and customizable active learning for named entity recognition in the digital humanities
Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, and Marie-Catherine de Marneffe. 2019 · 2019
Cited alongside, same era.
Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz. 2019 · 2019
Cited alongside, same era.
Journalist-in-the-loop: Continuous learning as a service for rumour analysis
Twin Karmakharm, Nikolaos Aletras, and Kalina Bontcheva. 2019 · 2019
Cited alongside, same era.
Low-resource deep entity resolution with transfer and active learning
Jungo Kasai, Kun Qian, Sairam Gurajada, Yunyao Li, and Lucian Popa. 2019 · 2019
Cited alongside, same era.
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Active learning by acquiring contrastive examples
Katerina Margatina, Giorgos Vernikos, Loïc Barrault, and Nikolaos Aletras. 2021 · 2021
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Temporal adaptation of BERT and performance on downstream document classification: Insights from social media
Paul Röttger and Janet Pierrehumbert. 2021 · 2021
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Active learning for sequence tagging with deep pre-trained models and Bayesian uncertainty estimates
Artem Shelmanov, Dmitri Puzyrev, Lyubov Kupriyanova, Denis Belyakov, Daniil Larionov, Nikita Khromov, Olga Kozlova, Ekaterina Artemova, Dmitry V. Dylov, and Alexander Panchenko. 2021 · 2021
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Diversity-aware batch active learning for dependency parsing
Tianze Shi, Adrian Benton, Igor Malioutov, and Ozan İrsoy. 2021 · 2021
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We need to talk about random splits
Anders Søgaard, Sebastian Ebert, Jasmijn Bastings, and Katja Filippova. 2021 · 2021
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Combining data-driven supervision with human-in-the-loop feedback for entity resolution
Wenpeng Yin, Shelby Heinecke, Jia Li, Nitish Shirish Keskar, Michael Jones, Shouzhong Shi, Stanislav Georgiev, Kurt Milich, Joseph Esposito, and Caiming Xiong. 2021 · 2021
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Cartography active learning
Mike Zhang and Barbara Plank. 2021 · 2021
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Towards understanding the behaviors of optimal deep active learning algorithms
Yilun Zhou, Adithya Renduchintala, Xian Li, Sida Wang, Yashar Mehdad, and Asish Ghoshal. 2021 · 2021
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Models in the loop: Aiding crowdworkers with generative annotation assistants
Max Bartolo, Tristan Thrush, Sebastian Riedel, Pontus Stenetorp, Robin Jia, and Douwe Kiela. 2022 · 2022
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, and Jack Clark. 2022 · 2022
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Improving alignment of dialogue agents via targeted human judgements
Amelia Glaese, Nat McAleese, Maja Trębacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, Lucy Campbell-Gillingham, Jonathan Uesato, Po-Sen Huang, Ramona Comanescu, Fan Yang, Abigail See, Sumanth Dathathri, Rory Greig, Charlie Chen, Doug Fritz, Jaume Sanchez Elias, Richard Green, Soňa Mokrá, Nicholas Fernando, Boxi Wu, Rachel Foley, Susannah Young, Iason Gabriel, William Isaac, John Mellor, Demis Hassabis, Koray Kavukcuoglu, Lisa Anne Hendricks, and Geoffrey Irving. 2022 · 2022
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Active learning on a budget: Opposite strategies suit high and low budgets
Guy Hacohen, Avihu Dekel, and Daphna Weinshall. 2022 · 2022
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Impact of stop sets on stopping active learning for text classification
Luke Kurlandski and Michael Bloodgood. 2022 · 2022
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Active learning over multiple domains in natural language tasks
Shayne Longpre, Julia Reisler, Edward Greg Huang, Yi Lu, Andrew Frank, Nikhil Ramesh, and Chris DuBois. 2022 · 2022
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On the importance of effectively adapting pretrained language models for active learning
Katerina Margatina, Loic Barrault, and Nikolaos Aletras. 2022 · 2022
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Chatgpt
OpenAI. 2022 · 2022
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Hitting the target: stopping active learning at the cost-based optimum
Zac Pullar-Strecker, Katharina Dost, Eibe Frank, and Jörg Wicker. 2022 · 2022
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Revisiting uncertainty-based query strategies for active learning with transformers
Christopher Schröder, Andreas Niekler, and Martin Potthast. 2022 · 2022
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Active learning helps pretrained models learn the intended task
Alex Tamkin, Dat Pham Nguyen, Salil Deshpande, Jesse Mu, and Noah Goodman. 2022 · 2022
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Analyzing dynamic adversarial training data in the limit
Eric Wallace, Adina Williams, Robin Jia, and Douwe Kiela. 2022 · 2022
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AcTune: Uncertainty-based active self-training for active fine-tuning of pretrained language models
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang. 2022 · 2022
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Adapting coreference resolution models through active learning
Michelle Yuan, Patrick Xia, Chandler May, Benjamin Van Durme, and Jordan Boyd-Graber. 2022 · 2022
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ALLSH: Active learning guided by local sensitivity and hardness
Shujian Zhang, Chengyue Gong, Xingchao Liu, Pengcheng He, Weizhu Chen, and Mingyuan Zhou. 2022b · 2022
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Deep reinforcement learning from human preferences
Paul Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2023 · 2023
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Dynamic benchmarking of masked language models on temporal concept drift with multiple views
Katerina Margatina, Shuai Wang, Yogarshi Vyas, Neha Anna John, Yassine Benajiba, and Miguel Ballesteros. 2023 · 2023
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OpenAI. 2023 · 2023
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Small-text: Active learning for text classification in python
Christopher Schröder, Lydia Müller, Andreas Niekler, and Martin Potthast. 2023 · 2023
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Investigating multi-source active learning for natural language inference
Ard Snijders, Douwe Kiela, and Katerina Margatina. 2023 · 2023
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Principle-driven self-alignment of language models from scratch with minimal human supervision
Zhiqing Sun, Yikang Shen, Qinhong Zhou, Hongxin Zhang, Zhenfang Chen, David Cox, Yiming Yang, and Chuang Gan. 2023 · 2023
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Active PETs: Active data annotation prioritisation for few-shot claim verification with pattern exploiting training
Xia Zeng and Arkaitz Zubiaga. 2023 · 2023
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Active imitation learning with noisy guidance
Kianté Brantley, Amr Sharaf, and Hal Daumé III. 2020 · 2093
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