[1] David Alvarez-Melis, Tommi Jaakkola, and Stefanie Jegelka. Structured optimal transport. In International conference on artificial intelligence and statistics, pages 1771-1780. PMLR, 2018.
[2] Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, and Jing Jiang. Co-pilot: Collaborative planning and reinforcement learning on sub-task curriculum. Advances in Neural Information Processing Systems, 34:10444-10456, 2021.
[3] Eric Arazo, Diego Ortego, Paul Albert, Noel OConnor, and Kevin McGuinness. Unsupervised label noise modeling and loss correction. In International conference on machine learning, pages 312-321. PMLR, 2019.
[4] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In International conference on machine learning, pages 233-242. PMLR, 2017.
[5] YM Asano, C Rupprecht, and A Vedaldi. Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations, 2019.
[6] Dara Bahri, Heinrich Jiang, and Maya Gupta. Deep k-nn for noisy labels. In International Conference on Machine Learning, pages 540–550. PMLR, 2020.
[7] Heinz H Bauschke and Adrian S Lewis. Dykstras algorithm with bregman projections: A convergence proof. Optimization, 48(4):409-427, 2000.
[8] Jean-David Benamou, Guillaume Carlier, Marco Cuturi, Luca Nenna, and Gabriel Peyre. Iterative bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2):A1111-A1138, 2015.
[9] Dimitri P Bertsekas. Nonlinear programming. Journal of the Operational Research Society, 48(3):334-334, 1997.
[10] Lev M Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR computational mathematics and mathematical physics, 7(3):200-217, 1967.
[11] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33:9912-9924, 2020.
[12] Wanxing Chang, Ye Shi, Hoang Tuan, and Jingya Wang. Unified optimal transport framework for universal domain adaptation. Advances in Neural Information Processing Systems, 35:29512-29524, 2022.
[13] Laetitia Chapel, Mokhtar Z Alaya, and Gilles Gasso. Partial optimal transport with applications on positive-unlabeled learning. Advances in Neural Information Processing Systems, 33:2903-2913, 2020.
[14] Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang. Understanding and utilizing deep neural networks trained with noisy labels. In International Conference on Machine Learning, pages 1062-1070. PMLR, 2019.
[15] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15750-15758, 2021.
[16] Ching-Yao Chuang, Stefanie Jegelka, and David Alvarez-Melis. Infoot: Information maximizing optimal transport. In International Conference on Machine Learning, pages 6228-6242. PMLR, 2023.
[17] Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy. Optimal transport for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:1853-1865, 2017.
[18] Ekin D Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V Le. Autoaugment: Learning augmentation strategies from data. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 113-123, 2019.
[19] Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in neural information processing systems, 26:2292-2300, 2013.
[20] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. IEEE, 2009.
[21] Richard L Dykstra. An algorithm for restricted least squares regression. Journal of the American Statistical Association, 78(384):837-842, 1983.
[22] Erik Englesson and Hossein Azizpour. Consistency regularization can improve robustness to label noise. In International Conference on Machine Learning Workshops, 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021.