[63] Mingkui Tan, Ivor W Tsang, Li Wang, Bart Vandereycken, and Sinno Jialin Pan. Riemannian pursuit for big matrix recovery. In International Conference on Machine Learning, pages 1539-1547. PMLR, 2014.
[64] Uthaipon Tantipongpipat, Samira Samadi, Mohit Singh, Jamie H Morgenstern, and Santosh S Vempala. Multi-criteria dimensionality reduction with applications to fairness. Advances in neural information processing systems, (32), 2019.
[65] Davoud Ataee Tarzanagh and Laura Balzano. Online bilevel optimization: Regret analysis of online alternating gradient methods. arXiv preprint arXiv:2207.02829, 2022.
[66] Davoud Ataee Tarzanagh, Laura Balzano, and Alfred O Hero. Fair structure learning in heterogeneous graphical models. arXiv preprint arXiv:2112.05128, 2021.
[67] Davoud Ataee Tarzanagh, Bojian Hou, Boning Tong, Qi Long, and Li Shen. Fairness-aware class imbalanced learning on multiple subgroups. In Uncertainty in Artificial Intelligence, pages 2123-2133. PMLR, 2023.
[68] Davoud Ataee Tarzanagh, Mingchen Li, Christos Thrampoulidis, and Samet Oymak. Fednest: Federated bilevel, minimax, and compositional optimization. In International Conference on Machine Learning, pages 21146-21179. PMLR, 2022.
[69] Xin M Tu, Donald S Burdick, Debra W Millican, and L Bruce McGown. Canonical correlation technique for rank estimation of excitation-emission matrixes. Analytical chemistry, 61(19):2219-2224, 1989.
[70] Viivi Uurtio, João M Monteiro, Jaz Kandola, John Shawe-Taylor, Delmiro Fernandez-Reyes, and Juho Rousu. A tutorial on canonical correlation methods. ACM Computing Surveys (CSUR), 50(6):1-33, 2017.
[71] GC Wang, N Lin, and B Zhang. Dimension reduction in functional regression using mixed data canonical correlation analysis. Stat Interface, 6:187-196, 2013.
[72] FV Waugh. Regressions between sets of variables. Econometrica, Journal of the Econometric Society, pages 290-310, 1942.
[73] M. W. Weiner, D. P. Veitch, P. S. Aisen, et al. The alzheimer's disease neuroimaging initiative: a review of papers published since its inception. *Alzheimers Dement*, 9(5):e111–94, 2013.
[74] Michael W Weiner, Dallas P Veitch, Paul S Aisen, et al. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimer's & Dementia, 13(4):e1-e85, 2017.
[75] Zaiwen Wen and Wotao Yin. A feasible method for optimization with orthogonality constraints. Mathematical Programming, 142(1-2):397-434, 2013.
[76] KW Wong, PCW Fung, and CC Lau. Study of the mathematical approximations made in the basis-correlation method and those made in the canonical-transformation method for an interacting Bose gas. Physical Review A, 22(3):1272, 1980.
[77] Florian Yger, Maxime Berar, Gilles Gasso, and Alain Rakotomamonjy. Adaptive canonical correlation analysis based on matrix manifolds. arXiv preprint arXiv:1206.6453, 2012.
[78] Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi. Fairness constraints: Mechanisms for fair classification. In Artificial intelligence and statistics, pages 962-970. PMLR, 2017.
[79] Yu Zhang, Guoxu Zhou, Jing Jin, Yangsong Zhang, Xingyu Wang, and Andrzej Cichocki. Sparse bayesian multiway canonical correlation analysis for eeg pattern recognition. Neurocomputing, 225:103-110, 2017.
[80] Xiaowei Zhuang, Zhengshi Yang, and Dietmar Cordes. A technical review of canonical correlation analysis for neuroscience applications. Human Brain Mapping, 41(13):3807-3833, 2020.
[81] Yongshuo Zong, Yongxin Yang, and Timothy Hospedales. Medfair: Benchmarking fairness for medical imaging. arXiv preprint arXiv:2210.01725, 2022.