[19] D. Kuhn, P. M. Esfahani, V. A. Nguyen, and S. Shafieezadeh-Abadeh. Wasserstein distributionally robust optimization: Theory and applications in machine learning. In Operations research & management science in the age of analytics, pages 130–166. Informs, 2019.
[20] Y. Kwon, W. Kim, J.-H. Won, and M. C. Paik. Principled learning method for wasserstein distributionally robust optimization with local perturbations. In International Conference on Machine Learning, pages 5567-5576. PMLR, 2020.
[21] Y. Le and X. Yang. Tiny imagenet visual recognition challenge. CS 231N, 7(7):3, 2015.
[22] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[23] F. Lin, X. Fang, and Z. Gao. Distributionally robust optimization: A review on theory and applications. Numerical Algebra, Control and Optimization, 12(1):159-212, 2022.
[24] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dólár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017.
[25] H. Liu, K. Han, V. V. Gayah, T. L. Friesz, and T. Yao. Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control. Transportation Research Part C: Emerging Technologies, 59:260-277, 2015.
[26] M. Liu, Z. Yuan, Y. Ying, and T. Yang. Stochastic auc maximization with deep neural networks. arXiv preprint arXiv:1908.10831, 2019.
[27] J. Lu and S. Steinerberger. Neural collapse with cross-entropy loss. arXiv preprint arXiv:2012.08465, 2020.
[28] P. Mohajerin Esfahani and D. Kuhn. Data-driven distributionally robust optimization using the wasserstein metric: Performance guarantees and tractable reformulations. Mathematical Programming, 171(1-2):115-166, 2018.
[29] M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of machine learning. MIT press, 2018.
[30] N. Mu and J. Gilmer. Mnist-c: A robustness benchmark for computer vision. arXiv preprint arXiv:1906.02337, 2019.
[31] H. Namkoong and J. C. Duchi. Variance-based regularization with convex objectives. Advances in neural information processing systems, 30, 2017.
[32] V. Papyan, X. Han, and D. L. Donoho. Prevalence of neural collapse during the terminal phase of deep learning training. Proceedings of the National Academy of Sciences, 117(40):24652-24663, 2020.
[33] A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019.
[34] H. Phan, T. Le, T. Phung, A. T. Bui, N. Ho, and D. Phung. Global-local regularization via distributional robustness. In International Conference on Artificial Intelligence and Statistics, pages 7644-7664. PMLR, 2023.
[35] Q. Qi, Y. Luo, Z. Xu, S. Ji, and T. Yang. Stochastic optimization of areas under precision-recall curves with provable convergence. Advances in Neural Information Processing Systems, 34:1752-1765, 2021.
[36] L. Rice, E. Wong, and Z. Kolter. Overfitting in adversarially robust deep learning. In International Conference on Machine Learning, pages 8093-8104. PMLR, 2020.
[37] D. Samuel and G. Chechik. Distributional robustness loss for long-tail learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9495-9504, 2021.
[38] A. Shapiro. Tutorial on risk neutral, distributionally robust and risk averse multistage stochastic programming. European Journal of Operational Research, 288(1):1-13, 2021.
[39] A. Sinha, H. Namkoong, R. Volpi, and J. Duchi. Certifying some distributional robustness with principled adversarial training. arXiv preprint arXiv:1710.10571, 2017.
[40] J. Sulam, R. Ben-Ari, and P. Kisilev. Maximizing auc with deep learning for classification of imbalanced mammogram datasets. In VCBM, pages 131-135, 2017.