[1] Abhijeet Awasthi, Sabyasachi Ghosh, Rasna Goyal, and Sunita Sarawagi. Learning from rules generalizing labeled exemplars. In International Conference on Learning Representations, 2020.
[2] Benedikt Boecking and Artur Dubrawski. Pairwise feedback for data programming. In Proceedings of NeurIPS '19 Workshop on Learning with Rich Experience (LIRE '19), December 2019.
[3] M. Herbert C. Rosenberg and H. Schneiderman. Semi-supervised learning by entropy minimization. In Semi-Supervised Self-Training of Object Detection Models, 2005.
[4] Salva Cachay, Benedikt Boecking, and Artur Dubrawski. End-to-end weak supervision. In Advances in Neural Information Processing Systems, volume 34, 2021.
[5] Inigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, and Ivan Vulić. Efficient intent detection with dual sentence encoders. In Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, pages 38-45, Online, July 2020. Association for Computational Linguistics.
[6] Mayee Chen, Benjamin Cohen-Wang, Stephen Mussmann, Frederic Sala, and Christopher Re. Comparing the value of labeled and unlabeled data in method-of-moments latent variable estimation. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 3286-3294. PMLR, 13-15 Apr 2021.
[7] Y. Ding, L. Wang, D. Fan, and B. Gong. A semi-supervised two-stage approach to learning from noisy labels. In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1215–1224, Los Alamitos, CA, USA, mar 2018. IEEE Computer Society.
[8] Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, and Prem Natarajan. Massive: A 1m-example multilingual natural language understanding dataset with 51 typologically-diverse languages, 2022.
[9] Chufan Gao, Mononito Goswami, Jieshi Chen, and Artur Dubrawski. Classifying unstructured clinical notes via automatic weak supervision. In Proceedings of the 7th Machine Learning for Healthcare Conference, volume 182 of Proceedings of Machine Learning Research, pages 673-690. PMLR, 05-06 Aug 2022.
[10] Yves Grandvalet and Yoshua Bengio. Semi-supervised learning by entropy minimization. In Advances in Neural Information Processing Systems, volume 17. MIT Press, 2004.
[11] Giannis Karamanolakis, Subhabrata Mukherjee, Guoqing Zheng, and Ahmed Hassan Awadallah. Self-training with weak supervision. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 845–863, Online, June 2021. Association for Computational Linguistics.
[12] Jan Kocón, Igor Cichecki, Oliwier Kaszyca, Mateusz Kochanek, Dominika Szydlo, Joanna Baran, Julita Bielaniewicz, Marcin Gruza, Arkadiusz Janz, Kamil Kanclerz, Anna Kocón, Bartlomiej Koptyra, Wiktoria Mieleszczenko-Kowszewicz, Piotr Miłkowski, Marcin Oleksy, Maciej Piasecki, Łukasz Radlinski, Konrad Wojtasik, Stanisław Wozniak, and Przemysław Kazienko. ChatGPT: Jack of all trades, master of none. Information Fusion, 99:101861, 2023.
[13] Kyeongbo Kong, Junggi Lee, Youngchul Kwak, Minsung Kang, Seong Gyun Kim, and Woo-Jin Song. Recycling: Semi-supervised learning with noisy labels in deep neural networks. IEEE Access, 7:66998-67005, 2019.
[14] Samuli Laine and Timo Aila. Temporal ensembling for semi-supervised learning. In International Conference on Learning Representations, 2017.