[16] David J Hand. Measuring classifier performance: a coherent alternative to the area under the roc curve. Machine learning, 77(1):103-123, 2009.
[17] James A Hanley and Barbara J McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143(1):29-36, 1982.
[18] James A Hanley and Barbara J McNeil. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology, 148(3):839-843, 1983.
[19] Huaying Hao, Huazhu Fu, Yanwu Xu, Jianlong Yang, Fei Li, Xiulan Zhang, Jiang Liu, and Yitian Zhao. Open-narrow-synechiae anterior chamber angle classification in as-oct sequences. arXiv preprint arXiv:2006.05367, 2020.
[20] José Hernández-Orallo, Peter Flach, and César Ferri Ramírez. A unified view of performance metrics: Translating threshold choice into expected classification loss. Journal of Machine Learning Research, 13:2813–2869, 2012.
[21] José Hernández-Orallo, Peter Flach, and César Ferri. Roc curves in cost space. Machine learning, 93(1):71-91, 2013.
[22] Thorsten Joachims. A support vector method for multivariate performance measures. In International Conference on Machine Learning, pages 377-384, 2005.
[23] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[24] Yunwen Lei and Yiming Ying. Stochastic proximal auc maximization. JMLP, 22(61):1-45, 2021.
[25] Jialiang Li and Jason P Fine. Weighted area under the receiver operating characteristic curve and its application to gene selection. Journal of the Royal Statistical Society: Series C (Applied Statistics), 59(4):673-692, 2010.
[26] Mingrui Liu, Xiaoxuan Zhang, Zaiyi Chen, Xiaoyu Wang, and Tianbao Yang. Fast stochastic auc maximization with  $o(1/n)$ -convergence rate. In ICML, pages 3189-3197. PMLR, 2018.
[27] Mingrui Liu, Zhuoning Yuan, Yiming Ying, and Tianbao Yang. Stochastic AUC maximization with deep neural networks. In International Conference on Learning Representations, 2020.
[28] Risheng Liu, Pan Mu, Xiaoming Yuan, Shangzhi Zeng, and Jin Zhang. A generic first-order algorithmic framework for bi-level programming beyond lower-level singleton. In International Conference on Machine Learning, pages 6305-6315. PMLR, 2020.
[29] Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao. Future frame prediction for anomaly detection-a new baseline. In CVPR, pages 6536-6545, 2018.
[30] Andreas Maurer and Massimiliano Pontil. Estimating weighted areas under the roc curve. Advances in Neural Information Processing Systems, 33:7733-7742, 2020.
[31] Donna Katzman McClish. Analyzing a portion of the roc curve. Medical decision making, 9(3): 190-195, 1989.
[32] Charles E Metz. Basic principles of roc analysis. In Seminars in nuclear medicine, volume 8, pages 283-298. Elsevier, 1978.
[33] Harikrishna Narasimhan and Shivani Agarwal. Support vector algorithms for optimizing the partial area under the roc curve. Neural Computation, 29(7):1919-1963, 2017.
[34] Sakrapee Paisitkriangkrai, Chunhua Shen, and Anton Van Den Hengel. Efficient pedestrian detection by directly optimizing the partial area under the roc curve. In Proceedings of the IEEE international conference on computer vision, pages 1057-1064, 2013.
[35] Margaret Sullivan Pepe and Mary Lou Thompson. Combining diagnostic test results to increase accuracy. Biostatistics, 1(2):123-140, 2000.