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Studies of active learning traditionally assume the target and source data stem from a single domain.
Discriminative active learning
Daniel Gissin and Shai Shalev-Shwartz. 2019 · 1907
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A mathematical theory of communication
Claude E. Shannon. 1948 · 1948
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Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan. 1996 · 1996
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2006 · 2006
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Reverse testing: an efficient framework to select amongst classifiers under sample selection bias
Wei Fan and Ian Davidson. 2006 · 2006
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Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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Active learning literature survey
Burr Settles. 2009 · 2009
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
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Learning word vectors for sentiment analysis
Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011 · 2011
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Active supervised domain adaptation
Avishek Saha, Piyush Rai, Hal Daumé, Suresh Venkatasubramanian, and Scott L. DuVall. 2011 · 2011
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Active learning
Burr Settles. 2012 · 2012
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Shift-pessimistic active learning using robust bias-aware prediction
Anqi Liu, Lev Reyzin, and Brian Ziebart. 2015 · 2015
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Yelp dataset challenge: Review rating prediction
Nabiha Asghar. 2016 · 2016
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XGBoost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin. 2016 · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Newsqa: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2016 · 2016
Cited alongside, same era.
Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
Cited alongside, same era.
Searchqa: A new q&a dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, V Ugur Guney, Volkan Cirik, and Kyunghyun Cho. 2017 · 2017
Cited alongside, same era.
Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani. 2017 · 2017
Cited alongside, same era.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
A review of domain adaptation without target labels
Wouter Marco Kouw and Marco Loog. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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An exploration of data augmentation and sampling techniques for domain-agnostic question answering
Shayne Longpre, Yi Lu, Zhucheng Tu, and Chris DuBois. 2019 · 2019
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Practical obstacles to deploying active learning
David Lowell, Zachary C Lipton, and Byron C Wallace. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Cited alongside, same era.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal. 2017 · 2017
Cited alongside, same era.
Learning to select data for transfer learning with bayesian optimization
Sebastian Ruder and Barbara Plank. 2017 · 2017
Cited alongside, same era.
Failing loudly: An empirical study of methods for detecting dataset shift
Stephan Rabanser, Stephan Günnemann, and Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
Strong baselines for neural semi-supervised learning under domain shift
Sebastian Ruder and Barbara Plank. 2018 · 2018
Cited alongside, same era.
Deep Bayesian active learning for natural language processing: Results of a large-scale empirical study
Aditya Siddhant and Zachary C. Lipton. 2018 · 2018
Cited alongside, same era.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Cited alongside, same era.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Multiqa: An empirical investigation of generalization and transfer in reading comprehension
Alon Talmor and Jonathan Berant. 2019 · 2019
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Active learning for bert: An empirical study
Liat Ein Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2020 · 2020
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Selective question answering under domain shift
Amita Kamath, Robin Jia, and Percy Liang. 2020 · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John D. Owens, and Yixuan Li. 2020 · 2020
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Multicqa: Zero-shot transfer of self-supervised text matching models on a massive scale
Andreas Rücklé, Jonas Pfeiffer, and Iryna Gurevych. 2020 · 2020
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Recommendation chart of domains for cross-domain sentiment analysis: Findings of a 20 domain study
Akash Sheoran, Diptesh Kanojia, Aditya Joshi, and Pushpak Bhattacharyya. 2020 · 2020
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Types of out-of-distribution texts and how to detect them
Udit Arora, William Huang, and He He. 2021 · 2021
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Multi-domain active learning: A comparative study
Rui He, Shan He, and Ke Tang. 2021 · 2021
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Active learning under pool set distribution shift and noisy data
Andreas Kirsch, Tom Rainforth, and Yarin Gal. 2021 · 2021
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Active learning under label shift
Eric Zhao, Anqi Liu, Animashree Anandkumar, and Yisong Yue. 2021 · 2021
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