[19] Christophe Giraud. Introduction to high-dimensional statistics. CRC Press, 2021.
[20] Christina Gopfert, Shai Ben-David, Olivier Bousquet, Sylvain Gelly, Ilya O. Tolstikhin, and Ruth Urner. When can unlabeled data improve the learning rate? In Annual Conference Computational Learning Theory, 2019.
[21] Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra. Scaling and benchmarking self-supervised visual representation learning. In International Conference on Computer Vision, 2019.
[22] Jean-Bastien Grill, Florian Strub, Florent Alché, Coretin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems 33, 2020.
[23] Yann LeCun and Corinna Cortes. MNIST handwritten digit database, 2010. URL http://yann.lecun.com/exdb/mnist/.
[24] Tianyang Li, Xinyang Yi, Constantine Carmanis, and Pradeep Ravikumar. Minimax Gaussian Classification & Clustering. In 20th International Conference on Artificial Intelligence and Statistics, 2017.
[25] Thomas Lucas, Philippe Weinzaepfel, and Gregory Rogez. Barely-supervised learning: semi-supervised learning with very few labeled images. In AAAI Conference on Artificial Intelligence, 2022.
[26] Pascal Massart. Concentration inequalities and model selection: Ecole d'Eté de Probabilités de Saint-Flour XXXIII-2003. Springer, 2007.
[27] Alexander Mey and Marco Loog. Improvability through semi-supervised learning: A survey of theoretical results. arXiv:1908.09574, 2020.
[28] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournaepau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 2011.
[29] John Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers, 1999.
[30] Joel Ratsaby and Santosh S. Venkatesh. Learning from a mixture of labeled and unlabeled examples with parametric side information. In Annual Conference on Learning Theory (COLT), 1995.
[31] Philippe Rigollet. Generalization error bounds in semi-supervised classification under the cluster assumption. Journal of Machine Learning Research, 2006.
[32] Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij. On causal and anticausal learning. In 29th International Conference on Machine Learning, 2012.
[33] Aarti Singh, Robert D. Nowak, and Xiaojin Zhu. Unlabeled data: Now it helps, now it doesn't. In Advances in Neural Information Processing Systems 21, 2008.
[34] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. FixMatch: Simplifying semi-supervised learning with consistency and confidence. In Advances in Neural Information Processing Systems, 2020.
[35] Erixhen Sula and Lizhong Zheng. On the semi-supervised expectation maximization. arXiv:2211.00537, 2022.
[36] Ilya O. Tolstikhin and David Lopez-Paz. Minimax lower bounds for realizable transductive classification. arXiv:1602.03027, 2016.