[44] Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Foundations of Machine Learning. MIT Press, 2018.
[45] Kevin P. Murphy. Machine learning - a probabilistic perspective. MIT Press, 2012.
[46] Nagarajan Natarajan, Inderjit S. Dhillon, Pradeep Ravikumar, and Ambuj Tewari. Learning with noisy labels. In NeurIPS, pages 1196-1204, 2013.
[47] Frank Nielsen. On the jensen-shannon symmetrization of distances relying on abstract means. Entropy, 21(5):485, 2019.
[48] Felix Otto and Michael Westdickenberg. Eulerian calculus for the contraction in the wasserstein distance. SIAM J. Math. Anal., 37(4):1227-1255, 2005.
[49] Namuk Park and Songkuk Kim. How do vision transformers work? In ICLR, pages 1-26, 2022.
[50] Gabriel Pereyra, George Tucker, Jan Chorowski, Lukasz Kaiser, and Geoffrey E. Hinton. Regularizing neural networks by penalizing confident output distributions. In ICLR, pages 1-11, 2017.
[51] Scott E. Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich. Training deep neuralnetworks on noisy labels with bootstrapping. In ICLR, 2015.
[52] Lukas Ruff, Nico Gornitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Robert A. Vandermeulen, Alexander Binder, Emmanuel Müller, and Marius Kloft. Deep one-class classification. In ICML, pages 4390-4399, 2018.
[53] Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning From Theory to Algorithms. Cambridge University Press, 2014.
[54] Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul von Bunau, and Motoaki Kawanabe. Direct importance estimation with model selection and its application to covariate shift adaptation. In NeurIPS, pages 1433-1440, 2007.
[55] Yu Sun, Xiaolong Wang, Zhuang Liu, John Miller, Alexei A. Efros, and Moritz Hardt. Test-time training with self-supervision for generalization under distribution shifts. In ICML, pages 9229-9248, 2020.
[56] Brendan van Rooyen, Aditya Krishna Menon, and Robert C. Williamson. Learning with symmetric label noise: The importance of being unhinged. In NeurIPS, pages 10-18, 2015.
[57] Haohan Wang, Songwei Ge, Zachary C. Lipton, and Eric P. Xing. Learning robust global representations by penalizing local predictive power. In NeurIPS, 2019.
[58] Jiaheng Wei, Hangyu Liu, Tongliang Liu, Gang Niu, Masashi Sugiyama, and Yang Liu. To smooth or not? when label smoothing meets noisy labels. In ICML, pages 23589-23614, 2022.
[59] Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama. Are anchor points really indispensable in label-noise learning? NeurIPS, pages 6835-6846, 2019.
[60] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. In ICLR, pages 1-13, 2018.
[61] Lijun Zhang, Tianbao Yang, and Rong Jin. Empirical risk minimization for stochastic convex optimization:  $\$0(1/n)$ -and  $\$0(1/n^2)$ -type of risk bounds. In  $COLT$ , volume 65, pages 1954-1979, 2017.
[62] Shengjia Zhao, Abhishek Sinha, Yutong He, Aidan Perreault, Jiaming Song, and Stefano Ermon. Comparing distributions by measuring differences that affect decision making. In ICLR, pages 1-20, 2022.
[63] Zhilin Zhao, Longbing Cao, and Kun-Yu Lin. Revealing the distributional vulnerability of discriminators by implicit generators. IEEE Trans. Pattern Anal. Mach. Intell., 45(7):8888-8901, 2023.