[MCR14] Sérgio Moro, Paulo Cortez, and Paulo Rita. A data-driven approach to predict the success of bank telemarketing. Decision Support Systems, 62:22-31, 2014.
[MSY+21] Binny Mathew, Punyajoy Saha, Seid Muhie Yimam, Chris Biemann, Pawan Goyal, and Animesh Mukherjee. Hatexplain: A benchmark dataset for explainable hate speech detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 14867-14875, 2021.
[MW18] Aditya Krishna Menon and Robert C Williamson. The cost of fairness in binary classification. In Conference on Fairness, accountability and transparency, pages 107-118. PMLR, 2018.
[ORDCR20] Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro, and Christopher Ré. Hidden stratification causes clinically meaningful failures in machine learning for medical imaging. In Proceedings of the ACM conference on health, inference, and learning, pages 151-159, 2020.
[PC+19] Gabriel Peyré, Marco Cuturi, et al. Computational optimal transport: With applications to data science. Foundations and Trends® in Machine Learning, 11(5-6):355-607, 2019.
$\left[\mathrm{RBE}^{+}18\right]$  Alexander Ratner, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. Snorkel: Rapid training data creation with weak supervision. In Proceedings of the 44th International Conference on Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, 2018.
$\left[\mathrm{RHD}^{+}19\right]$  A. J. Ratner, B. Hancock, J. Dunnmon, F. Sala, S. Pandey, and C. Ré. Training complex models with multi-task weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, 2019.
$\left[\mathrm{RHD}^{+}19\right]$  Alexander Ratner, Braden Hancock, Jared Dunnmon, Frederic Sala, Shreyash Pandey, and Christopher Ré. Training complex models with multi-task weak supervision. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4763-4771, 2019.
$\left[\mathrm{RKH}^{+}21\right]$  Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[RSW+16] A. J. Ratner, Christopher M. De Sa, Sen Wu, Daniel Selsam, and C. Ré. Data programming: Creating large training sets, quickly. In Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 2016.
[SDA+20] Nimit Sohoni, Jared Dunnmon, Geoffrey Angus, Albert Gu, and Christopher Ré. No subclass left behind: Fine-grained robustness in coarse-grained classification problems. Advances in Neural Information Processing Systems, 33:19339-19352, 2020.
[SLV+22] Changho Shin, Winfred Li, Harit Vishwakarma, Nicholas Carl Roberts, and Frederic Sala. Universalizing weak supervision. In International Conference on Learning Representations (ICLR), 2022.
[SMBN21] Nian Si, Karthyek Murthy, Jose Blanchet, and Viet Anh Nguyen. Testing group fairness via optimal transport projections. In International Conference on Machine Learning, pages 9649–9659. PMLR, 2021.
[SNS+21] Sahil Singla, Besmira Nushi, Shital Shah, Ece Kamar, and Eric Horvitz. Understanding failures of deep networks via robust feature extraction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12853-12862, 2021.