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Weak Supervision (WS) techniques allow users to efficiently create large training datasets by programmatically labeling data with heuristic sources of supervision.
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Snorkel: Rapid training data creation with weak supervision. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases , Vol. 11. NIH Public Access, 269
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017 · 2017
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Hybridization of Active Learning and Data Programming for Labeling Large Industrial Datasets
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Snuba: Automating weak supervision to label training data. In Proceedings of the VLDB Endowment. International Conference on Very Large Data Bases , Vol. 12. NIH Public Access, 223
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Snorkel drybell: A case study in deploying weak supervision at industrial scale. In SIGMOD (Industrial) . 362–375
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Osprey: Weak Supervision of Imbalanced Extraction Problems without Code
Eran Bringer, Abraham Israeli, Yoav Shoham, Alexander J. Ratner, and Christopher Ré. 2019 · 2019
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Interactive Programmatic Labeling for Weak Supervision
Benjamin Cohen-Wang, Steve Mussmann, Alexander Ratner, and Christopher Ré. 2019 · 2019
Denoising Multi-Source Weak Supervision for Neural Text Classification. In Findings of EMNLP
Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell, and Chao Zhang. 2020 · 2020
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Weakly supervised sequence tagging from noisy rules. In AAAI , Vol. 34. 5570–5578
Esteban Safranchik, Shiying Luo, and Stephen Bach. 2020 · 2020
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Active WeaSuL: Improving Weak Supervision with Active Learning
Samantha Biegel, Rafah El-Khatib, Luiz Otavio Vilas Boas Oliveira, Max Baak, and Nanne Aben. 2021 · 2021
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Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling. In International Conference on Learning Representations
Benedikt Boecking, Willie Neiswanger, Eric Xing, and Artur Dubrawski. 2021 · 2021
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End-to-End Weak Supervision. In Advances in Neural Information Processing Systems , A. Beygelzimer, Y. Dauphin, P. Liang, and J. Wortman Vaughan (Eds.)
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Cited alongside, same era.
Training complex models with multi-task weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 4763–4771
Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré. 2019 · 2019
Cited alongside, same era.
Overton: A data system for monitoring and improving machine-learned products
Christopher Ré, Feng Niu, Pallavi Gudipati, and Charles Srisuwananukorn. 2019 · 2019
Cited alongside, same era.
Multi-Resolution Weak Supervision for Sequential Data. In NeurIPS , Vol. 32
Paroma Varma, Frederic Sala, Shiori Sagawa, Jason Fries, Daniel Fu, Saelig Khattar, Ashwini Ramamoorthy, Ke Xiao, Kayvon Fatahalian, James Priest, and Christopher Ré. 2019 · 2019
Cited alongside, same era.
Learning from Rules Generalizing Labeled Exemplars. In International Conference on Learning Representations
Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. 2020 · 2020
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Goggles: Automatic image labeling with affinity coding. In SIGMOD . 1717–1732
Nilaksh Das, Sanya Chaba, Renzhi Wu, Sakshi Gandhi, Duen Horng Chau, and Xu Chu. 2020 · 2020
Cited alongside, same era.
Fast and three-rious: Speeding up weak supervision with triplet methods. In International Conference on Machine Learning . PMLR, 3280–3291
Daniel Fu, Mayee Chen, Frederic Sala, Sarah Hooper, Kayvon Fatahalian, and Christopher Ré. 2020 · 2020
Cited alongside, same era.
Cut out the annotator, keep the cutout: better segmentation with weak supervision. In ICLR
Sarah Hooper, Michael Wornow, Ying Hang Seah, Peter Kellman, Hui Xue, Frederic Sala, Curtis Langlotz, and Christopher Re. 2020 · 2020
Cited alongside, same era.
Salva Rühling Cachay, Benedikt Boecking, and Artur Dubrawski. 2021 · 2021
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Adaptive rule discovery for labeling text data. In Proceedings of the 2021 International Conference on Management of Data . 2217–2225
Sainyam Galhotra, Behzad Golshan, and Wang-Chiew Tan. 2021 · 2021
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Weakly Supervised Named Entity Tagging with Learnable Logical Rules. In ACL . 4568–4581
Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley, and Zhe Feng. 2021 · 2021
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Semi-Supervised Data Programming with Subset Selection. In FINDINGS
Ayush Maheshwari, Oishik Chatterjee, Krishnateja Killamsetty, Ganesh Ramakrishnan, and Rishabh K. Iyer. 2021 · 2021
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Adversarial Multi Class Learning under Weak Supervision with Performance Guarantees. In Proceedings of the 38th International Conference on Machine Learning (Proceedings of Machine Learning Research) , Marina Meila and Tong Zhang (Eds.), Vol. 139. PMLR, 7534–7543
Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H Bach, and Eli Upfal. 2021 · 2021
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Automatic Synthesis of Diverse Weak Supervision Sources for Behavior Analysis
Albert Tseng, Jennifer J Sun, and Yisong Yue. 2021 · 2021
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WRENCH: A Comprehensive Benchmark for Weak Supervision. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner. 2021 · 2021
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GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition. In EACL . 3636–3649
Xinyan Zhao, Haibo Ding, and Zhe Feng. 2021 · 2021
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Universalizing Weak Supervision. In ICLR
Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Roberts, and Frederic Sala. 2022 · 2022
Closest in time.
A Survey on Programmatic Weak Supervision
Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, and Alexander Ratner. 2022a · 2022
Closest in time.