[12] Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8440–8451, Online, July 2020. Association for Computational Linguistics. doi: 10.18653/v1/2020.acl-main.747. URL https://aclanthology.org/2020.acl-main.747
[13] Man-Ching Yuen, Irwin King, and Kwong-Sak Leung. A survey of crowdsourcing systems. In 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing, pages 766-773. IEEE, 2011.
[14] Jieyu Zhang, Cheng-Yu Hsieh, Yue Yu, Chao Zhang, and Alexander Ratner. A survey on programmatic weak supervision. arXiv preprint arXiv:2202.05433, 2022.
[15] Mononito Goswami, Benedikt Boecking, and Artur Dubrawski. Weak supervision for affordable modeling of electrocardiogram data. In AMIA Annual Symposium Proceedings, volume 2021, page 536. American Medical Informatics Association, 2021.
[16] Joshua C Peterson, Ruairidh M Battleday, Thomas L Griffiths, and Olga Russakovsky. Human uncertainty makes classification more robust. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9617-9626, Los Alamitos, CA, USA, nov 2019. IEEE Computer Society. doi: 10.1109/ICCV.2019.00971. URL https://doi.ieeecomputersociety.org/10.1109/ICCV.2019.00971
[17] Lucas Beyer, Olivier J Henaff, Alexander Kolesnikov, Xiaohua Zhai, and Aaron van den Oord. Are we done with imagenet? arXiv preprint arXiv:2006.07159, 2020.
[18] Daniele Roberto Giacobbe, Alessio Signori, Filippo Del Puente, Sara Mora, Luca Carmisciano, Federica Briano, Antonio Vena, Lorenzo Ball, Chiara Robba, Paolo Pelosi, et al. Early detection of sepsis with machine learning techniques: a brief clinical perspective. Frontiers in medicine, 8:617486, 2021.
[19] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In International conference on machine learning, pages 233-242. PMLR, 2017.
[20] Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM, 64(3): 107-115, 2021.
[21] Jialu Wang, Yang Liu, and Caleb Levy. Fair classification with group-dependent label noise. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 526-536, 2021.
[22] Songhua Wu, Mingming Gong, Bo Han, Yang Liu, and Tongliang Liu. Fair classification with instance-dependent label noise. In Conference on Causal Learning and Reasoning, pages 927-943. PMLR, 2022.
[23] Curtis Northcutt, Lu Jiang, and Isaac Chuang. Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research, 70:1373-1411, 2021.
[24] Geoff Pleiss, Tianyi Zhang, Ethan Elenberg, and Kilian Q. Weinberger. Identifying mislabeled data using the area under the margin ranking. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546.
[25] Curtis G. Northcutt, Tailin Wu, and Isaac L. Chuang. Learning with confident examples: Rank pruning for robust classification with noisy labels. In Proceedings of the Thirty-Third Conference on Uncertainty in Artificial Intelligence, UAI'17. AUAI Press, 2017. URL http://auai.org/uai2017/proceedings/papers/35.pdf