[18] Christie, G., Fendley, N., Wilson, J., and Mukherjee, R. (2018). Functional map of the world. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[19] Cortes, C., Mansour, Y., and Mohri, M. (2010). Learning Bounds for Importance Weighting. In Advances in Neural Information Processing Systems (NIPS).
[20] Cortes, C. and Mohri, M. (2014). Domain adaptation and sample bias correction theory and algorithm for regression. Theoretical Computer Science, 519.
[21] Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V. (2020). Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 702-703.
[22] Darlow, L. N., Crowley, E. J., Antoniou, A., and Storkey, A. J. (2018). Cinic-10 is not imagenet or CIFar-10. arXiv preprint arXiv:1810.03505.
[23] Deledalle, C.-A., Denis, L., Tabti, S., and Tupin, F. (2017). Closed-form expressions of the eigen decomposition of  $2 \times 2$  and  $3 \times 3$  Hermitian matrices. PhD thesis, Université de Lyon.
[24] DeVries, T. and Taylor, G. W. (2017). Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552.
[25] Elkan, C. and Noto, K. (2008). Learning classifiers from only positive and unlabeled data. In International Conference Knowledge Discovery and Data Mining (KDD), pages 213-220.
[26] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016). Domain-adversarial training of neural networks. The journal of machine learning research.
[27] Gardner, J., Pleiss, G., Weinberger, K. Q., Bindel, D., and Wilson, A. G. (2018). Gpytorch: Blackbox matrix-matrix gaussian process inference withgpu acceleration. In Advances in Neural Information Processing Systems (NeurIPS).
[28] Garg, S., Balakrishnan, S., and Lipton, Z. (2022a). Domain adaptation under open set label shift. In Advances in Neural Information Processing Systems (NeurIPS).
[29] Garg, S., Balakrishnan, S., Lipton, Z., Neyshabur, B., and Sedghi, H. (2022b). Leveraging unlabeled data to predict out-of-distribution performance. In International Conference on Learning Representations (ICLR).
[30] Garg, S., Erickson, N., Sharpnack, J., Smola, A., Balakrishnan, S., and Lipton, Z. (2023). Rlsbench: A large-scale empirical study of domain adaptation under relaxed label shift. In International Conference on Machine Learning (ICML).
[31] Garg, S., Wu, Y., Balakrishnan, S., and Lipton, Z. (2020). A unified view of label shift estimation. In Advances in Neural Information Processing Systems (NeurIPS).
[32] Garg, S., Wu, Y., Smola, A., Balakrishnan, S., and Lipton, Z. (2021). Mixture proportion estimation and PU learning: A modern approach. In Advances in Neural Information Processing Systems (NeurIPS).
[33] Garrido, Q., Chen, Y., Bardes, A., Najman, L., and Lecun, Y. (2022). On the duality between contrastive and non-contrastive self-supervised learning. arXiv preprint arXiv:2206.02574.
[34] Grandvalet, Y. and Bengio, Y. (2006). Entropy regularization.
[35] Gretton, A., Smola, A. J., Huang, J., Schmittfull, M., Borgwardt, K. M., and Scholkopf, B. (2009). Covariate Shift by Kernel Mean Matching. Journal of Machine Learning Research (JMLR).
[36] Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al. (2020). Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284.