[19] Y. Geifman and R. El-Yaniv. Selective classification for deep neural networks. In NeurIPS, 2017.
[20] Y. Geifman and R. El-Yaniv. Selectivenet: A deep neural network with an integrated reject option. In ICML, pages 2151-2159, 2019.
[21] X. Geng, C. Yin, and Z.-H. Zhou. Facial age estimation by learning from label distributions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(10):2401-2412, 2013.
[22] Y. Grandvalet, A. Rakotomamonjy, J. Keshet, and S. Canu. Support vector machines with a reject option. In NeurIPS, 2008.
[23] S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu. A survey of deep learning techniques for autonomous driving. Journal of Field Robotics, 37(3):362-386, 2020.
[24] C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger. On calibration of modern neural networks. In ICML, pages 1321-1330, 2017.
[25] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016.
[26] C. M. Jarque and A. K. Bera. Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3):255-259, 1980.
[27] W. Jiang, Y. Zhao, and Z. Wang. Risk-controlled selective prediction for regression deep neural network models. In IJCNN, pages 1-8, 2020.
[28] M. A. Kadampur and S. Al Riyaee. Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked, 18:100282, 2020.
[29] K. Kersting, C. Plagemann, P. Pfaff, and W. Burgard. Most likely heteroscedastic gaussian process regression. In ICML, pages 393-400, 2007.
[30] M. Lázaro-Gredilla and M. K. Titsias. Variational heteroscedastic gaussian process regression. In ICML, pages 841-848, 2011.
[31] J. K. Lee, Y. Bu, D. Rajan, P. Sattigeri, R. Panda, S. Das, and G. W. Wornell. Fair selective classification via sufficiency. In ICML, 2021.
[32] H. Liu, Y.-S. Ong, and J. Cai. Large-scale heteroscedastic regression via gaussian process. IEEE Transactions on Neural Networks and Learning Systems, 32(2):708-721, 2020.
[33] A. L. Martel, S. Nofech-Mozes, S. Salama, S. Akbar, and M. Peikari. Assessment of residual breast cancer cellularity after neoadjuvant chemotherapy using digital pathology [data], 2019.
[34] A. Maurer. A vector-contraction inequality for rademacher complexities. In ALT, pages 3-17, 2016.
[35] S. Moschoglou, A. Papaioannou, C. Sagonas, J. Deng, I. Kotsia, and S. Zafeiriou. Agedb: The first manually collected, in-the-wild age database. In CVPRW, pages 1997-2005, 2017.
[36] C. Ni, N. Charoenphakdee, J. Honda, and M. Sugiyama. On the calibration of multiclass classification with rejection. In NeurIPS, 2019.
[37] T. Pietraszek. Optimizing abstaining classifiers using roc analysis. In ICML, pages 665-672, 2005.
[38] A. Pugnana. Topics in selective classification. In AAAI, pages 16129-16130, 2023.
[39] H. G. Ramaswamy, A. Tewari, and S. Agarwal. Consistent algorithms for multiclass classification with a reject option. arXiv preprint arXiv:1505.04137, 2015.
[40] H. G. Ramaswamy, A. Tewari, and S. Agarwal. Consistent algorithms for multiclass classification with an abstain option. Electronic Journal of Statistics, 12(1):530-554, 2018.