[16] J. Quinonero-Candela, M. Sugiyama, A. Schwaighofer, and N. D. Lawrence, Dataset shift in machine learning. Mit Press, 2008.
[17] B. Sun and K. Saenko, "Deep CORAL: Correlation alignment for deep domain adaptation," arXiv:1607.01719 [cs.CV].
[18] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, "Domain-adversarial training of neural networks," arXiv: 1505.07818 [stat.ML].
[19] M. Long, Y. Cao, J. Wang, and M. I. Jordan, "Learning transferable features with deep adaptation networks," arXiv:1502.02791 [cs.LG].
[20] B. B. Damodaran, B. Kellenberger, R. Flamary, D. Tuia, and N. Courty, "DeepJDOT: Deep joint distribution optimal transport for unsupervised domain adaptation," arXiv:1803.10081 [cs.CV].
[21] E. F. Montesuma and F. M. N. Mboula, "Wasserstein barycenter for multi-source domain adaptation," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 16785-16793. 2021.
[22] D. Chen, D. Wang, T. Darrell, and S. Ebrahimi, "Contrastive test-time adaptation," arXiv:2204.10377 [cs.CV].
[23] R. Bhatia, T. Jain, and Y. Lim, "On the bures-wasserstein distance between positive definite matrices," Expositiones Mathematicae 37 no. 2, (2019) 165-191.
[24] G. Peyre and M. Cuturi, "Computational optimal transport," arXiv:1803.00567 [stat.ML].
[25] P. J. Forrester and M. Kieburg, “Relating the Bures measure to the Cauchy two-matrix model,” Communications in Mathematical Physics 342 no. 1, (Oct, 2015) 151–187.
[26] K. Nadjahi, A. Durmus, P. E. Jacob, R. Badeau, and U. Simsekli, "Fast approximation of the sliced-Wasserstein distance using concentration of random projections," Advances in Neural Information Processing Systems 34 (2021) 12411-12424.
[27] R. Flamary, K. Lounici, and A. Ferrari, "Concentration bounds for linear Monge mapping estimation and optimal transport domain adaptation," arXiv:1905.10155 [stat.ML].
[28] N. Fournier and A. Guillin, "On the rate of convergence in wasserstein distance of the empirical measure," Probability theory and related fields 162 no. 3-4, (2015) 707-738.
[29] M. Agueh and G. Carlier, “Barycenters in the wasserstein space,” SIAM Journal on Mathematical Analysis 43 no. 2, (2011) 904–924, https://doi.org/10.1137/100805741. https://doi.org/10.1137/100805741.
[30] Y. Mroueh, "Wasserstein style transfer," arXiv:1905.12828 [cs.LG].
[31] A. Kroshnin, V. Spokoiny, and A. Suvorikova, "Statistical inference for Bures-Wasserstein barycenters," The Annals of Applied Probability 31 no. 3, (2021) 1264-1298.
[32] P. Welch, "The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms," IEEE Transactions on Audio and Electroacoustics 15 no. 2, (1967) 70-73.
[33] A. V. Oppenheim, A. S. Willsky, and S. H. Nawab, Signals & Systems (2nd Ed.). Prentice-Hall, Inc., USA, 1996.
[34] R. Bhatia, S. Gaubert, and T. Jain, "Matrix versions of the hellinger distance," Letters in Mathematical Physics 109 (2019) 1777-1804.
[35] N. Courty, R. Flamary, and D. Tuia, “Domain adaptation with regularized optimal transport,” in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I 14, pp. 274-289, Springer. 2014.
[36] H. Phan, O. Y. Chen, M. C. Tran, P. Koch, A. Mertins, and M. D. Vos, "XSleepNet: Multi-view sequential model for automatic sleep staging," IEEE Transactions on Pattern Analysis and Machine Intelligence 44 no. 09, (Sep, 2022) 5903-5915.
[37] S. Quan, B. Howard, et al., “The sleep heart health study: Design, rationale, and methods,” Sleep 20 (01, 1998) 1077–85.