[15] Hunter Lang, Aravindan Vijayaraghavan, and David Sontag. Training subset selection for weak supervision. In Advances in Neural Information Processing Systems, volume 35, pages 16023-16036. Curran Associates, Inc., 2022.
[16] D.H. Lee. The simple and efficient semi-supervised learning method for deep neural networks. In ICML Workshop on Challenges in Representation Learning, 2013.
[17] Junnan Li, Richard Socher, and Steven C.H. Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In International Conference on Learning Representations, 2020.
[18] Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Rishabh K. Iyer, and Ganesh Ramakrishnan. Data programming using semi-supervision and subset selection. CoRR, abs/2008.09887, 2020.
[19] Alessio Mazzetto, Cyrus Cousins, Dylan Sam, Stephen H Bach, and Eli Upfal. Adversarial multi class learning under weak supervision with performance guarantees. In Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 7534-7543. PMLR, 18-24 Jul 2021.
[20] Alessio Mazzetto, Dylan Sam, Andrew Park, Eli Upfal, and Stephen Bach. Semi-supervised aggregation of dependent weak supervision sources with performance guarantees. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, volume 130 of Proceedings of Machine Learning Research, pages 3196-3204. PMLR, 13-15 Apr 2021.
[21] Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. Virtual adversarial training: A regularization method for supervised and semi-supervised learning, 2017.
[22] Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow. Realistic evaluation of deep semi-supervised learning algorithms. In Advances in Neural Information Processing Systems, volume 31, 2018.
[23] Rattana Pukdee, Dylan Sam, Pradeep Kumar Ravikumar, and Nina Balcan. Label propagation with weak supervision. In The Eleventh International Conference on Learning Representations, 2023.
[24] Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré. Training complex models with multi-task weak supervision. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):4763-4771, Jul. 2019.
[25] Alexander J Ratner, Christopher M De Sa, Sen Wu, Daniel Selsam, and Christopher Ré. Data programming: Creating large training sets, quickly. In Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016.
[26] Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell, and Chao Zhang. Denoising multi-source weak supervision for neural text classification. In *Findings of the Association for Computational Linguistics: EMNLP* 2020, pages 3739-3754, Online, November 2020. Association for Computational Linguistics.
[27] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017.
[28] Jiaheng Wei, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu. Learning with noisy labels revisited: A study using real-world human annotations. In International Conference on Learning Representations, 2022.
[29] Qizhe Xie, Zihang Dai, Eduard Hovy, Thang Luong, and Quoc Le. Unsupervised data augmentation for consistency training. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 6256-6268. Curran Associates, Inc., 2020.
[30] Yue Yu, Simiao Zuo, Haoming Jiang, Wendi Ren, Tuo Zhao, and Chao Zhang. Fine-tuning pretrained language model with weak supervision: A contrastive-regularized self-training approach. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1063–1077, 2021.