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Existing weak supervision approaches use all the data covered by weak signals to train a classifier.
Probability of error of some adaptive pattern-recognition machines
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Maximum likelihood estimation of observer error-rates using the em algorithm
Alexander Philip Dawid and Allan M Skene · 1979
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Learning from noisy examples
Dana Angluin and Philip Laird · 1988
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky · 1995
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Introduction to statistical learning theory
Olivier Bousquet, Stéphane Boucheron, and Gábor Lugosi · 2003
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Identifying and handling mislabelled instances
Fabrice Muhlenbach, Stéphane Lallich, and Djamel A Zighed · 2004
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Pruning training sets for learning of object categories
Anelia Angelova, Yaser Abu-Mostafam, and Pietro Perona · 2005
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Setred: Self-training with editing
Ming Li and Zhi-Hua Zhou · 2005
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Cotrade: Confident co-training with data editing
Min-Ling Zhang and Zhi-Hua Zhou · 2011
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Self-training with selection-by-rejection
Yan Zhou, Murat Kantarcioglu, and Bhavani Thuraisingham · 2012
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Classification with asymmetric label noise: Consistency and maximal denoising
Clayton Scott, Gilles Blanchard, and Gregory Handy · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts · 2013
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Learning from corrupted binary labels via class-probability estimation
Aditya Menon, Brendan Van Rooyen, Cheng Soon Ong, and Bob Williamson · 2015
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Data programming: Creating large training sets, quickly
Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré · 2016
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Making deep neural networks robust to label noise: A loss correction approach
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu · 2017
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Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré · 2017
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Learning non-discriminatory predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
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Adversarial label learning
Chidubem Arachie and Bert Huang · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Chexclusion: Fairness gaps in deep chest x-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2020
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Learning with instance-dependent label noise: A sample sieve approach
Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
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Self-training with weak supervision
Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, and Ahmed Hassan · 2021
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Semi-supervised data programming with subset selection
Ayush Maheshwari, Oishik Chatterjee, Krishnateja Killamsetty, Ganesh Ramakrishnan, and Rishabh Iyer · 2021
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Confident learning: Estimating uncertainty in dataset labels
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
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Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng · 2019
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Dividemix: Learning with noisy labels as semi-supervised learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Training complex models with multi-task weak supervision
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Curtis Northcutt, Lu Jiang, and Isaac Chuang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Fine-tuning pre-trained language model with weak supervision: A contrastive-regularized self-training approach
Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang · 2021
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Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner · 2021
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A good representation detects noisy labels
Zhaowei Zhu, Zihao Dong, Hao Cheng, and Yang Liu · 2021
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Shoring up the foundations: Fusing model embeddings and weak supervision
Mayee F Chen, Daniel Yang Fu, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, and Christopher Re · 2022
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Machine Learning Core
Steven Horng · 2022
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Co-training improves prompt-based learning for large language models
Hunter Lang, Monica N Agrawal, Yoon Kim, and David Sontag · 2022
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LOPS: Learning order inspired pseudo-label selection for weakly supervised text classification
Dheeraj Mekala, Chengyu Dong, and Jingbo Shang · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al · 2022
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