[1] Z. Wan, X. Xia, D. Lo, and G. C. Murphy, "How does machine learning change software development practices?" IEEE Transactions on Software Engineering, vol. 47, no. 9, pp. 1857-1871, 2021.
[2] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, "A survey on bias and fairness in machine learning," ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1-35, 2021.
[3] S. H. Silva and P. Najafirad, "Opportunities and challenges in deep learning adversarial robustness: A survey," arXiv preprint arXiv:2007.00753, 2020.
[4] Q. Yang, T. D. Simão, S. H. Tindemans, and M. T. Spanan, "Safety-constrained reinforcement learning with a distributional safety critic," Machine Learning, vol. 112, no. 3, pp. 859-887, 2023.
[5] C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel, “Fairness through awareness,” in Proceedings of the 3rd innovations in theoretical computer science conference, 2012, pp. 214–226.
[6] M. Donini, L. Oneto, S. Ben-David, J. S. Shawe-Taylor, and M. Pontil, "Empirical risk minimization under fairness constraints," Advances in neural information processing systems, vol. 31, 2018.
[7] L. Chamon and A. Ribeiro, "Probably approximately correct constrained learning," Advances in Neural Information Processing Systems, vol. 33, 2020.
[8] L. F. Chamon, S. Paternain, M. Calvo-Fullana, and A. Ribeiro, “Constrained learning with non-convex losses,” IEEE Transactions on Information Theory, 2022.
[9] S. Paternain, L. Chamon, M. Calvo-Fullana, and A. Ribeiro, “Constrained reinforcement learning has zero duality gap,” Advances in Neural Information Processing Systems, vol. 32, 2019.
[10] C. Tran, F. Fioretto, and P. Van Hentenryck, "Differentially private and fair deep learning: A lagrangian dual approach," in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 11, 2021, pp. 9932-9939.
[11] F. Fioretto, P. Van Hentenryck, T. W. Mak, C. Tran, F. Baldo, and M. Lombardi, “Lagrangian duality for constrained deep learning,” in Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part V. Springer, 2021, pp. 118–135.
[12] A. Robey, L. Chamon, G. J. Pappas, H. Hassani, and A. Ribeiro, "Adversarial robustness with semi-infinite constrained learning," Advances in Neural Information Processing Systems, vol. 34, pp. 6198-6215, 2021.
[13] A. Robey, G. J. Pappas, and H. Hassani, "Model-based domain generalization," Advances in Neural Information Processing Systems, vol. 34, pp. 20210-20229, 2021.
[14] E. Diana, W. Gill, M. Kearns, K. Kenthapadi, and A. Roth, "Minimax group fairness: Algorithms and experiments," in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 66-76.
[15] Y. Xu, A. Noy, M. Lin, Q. Qian, H. Li, and R. Jin, "Wemix: How to better utilize data augmentation," arXiv preprint arXiv:2010.01267, 2020.
[16] L. Peng, P. V. Giampouras, and R. Vidal, "The ideal continual learner: An agent that never forgets," arXiv preprint arXiv:2305.00316, 2023.
[17] J. Gallego-Posada, J. Ramirez, A. Erraqabi, Y. Bengio, and S. Lacoste-Julien, "Controlled sparsity via constrained optimization or: How i learned to stop tuning penalties and love constraints," 2022. [Online]. Available: https://arxiv.org/abs/2208.04425