[1] Melissa J. Azur, Elizabeth A. Stuart, Constantine Frangakis, and Philip J. Leaf. Multiple imputation by chained equations: what is it and how does it work? International Journal of Methods in Psychiatric Research, 20, 2011.
[2] David Chas Bolton, Parisa Shokouhi, Bertrand Philippe Gerard Rouet-Leduc, Claudia Hulbert, Jacques Rivière, Chris Marone, and Paul Allan Johnson. Characterizing acoustic signals and searching for precursors during the laboratory seismic cycle using unsupervised machine learning. Seismological Research Letters, 2019.
[3] Kabir Aladin Chandrasekher, Ahmed El Alaoui, and Andrea Montanari. Imputation for high-dimensional linear regression. arXiv preprint arXiv:2001.09180, 2020.
[4] Wei-Ning Chen, Peter Kairouz, and Ayfer Özgür. Breaking the communication-privacy-accuracy trilemma. IEEE Transactions on Information Theory, 69:1261–1281, 2020.
[5] Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang. Secureboost: A lossless federated learning framework. IEEE Intelligent Systems, 36:87-98, 2019.
[6] Giacomo Curzi, Dario Modenini, and Paolo Tortora. Large constellations of small satellites: A survey of near future challenges and missions. Aerospace, 2020.
[7] Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt. Retiring adult: New datasets for fair machine learning. In Neural Information Processing Systems, 2021.
[8] John C. Duchi, Michael I. Jordan, Martin J. Wainwright, and Yuchen Zhang. Optimality guarantees for distributed statistical estimation. arXiv: Information Theory, 2014.
[9] Adrià Gascon, Phillip Schoppmann, Borja Balle, Mariana Raykova, Jack Doerner, Samee Zahr, and David Evans. Privacy-preserving distributed linear regression on high-dimensional data. Proceedings on Privacy Enhancing Technologies, 2017:345 - 364, 2017.
[10] Elisabeth Gassiat. Revisiting the van Trees inequality in the spirit of Hajek and Le Cam. 2014.
[11] Stephen Hardy, Wilko Henecka, Hamish Ivey-Law, Richard Nock, Giorgio Patrini, Guillaume Smith, and Brian Thorne. Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption. ArXiv, abs/1711.10677, 2017.
[12] Zhenbang Jiao, Hu Sun, Xiantong Wang, Ward B. Manchester, Tamas I. I. Gombosi, Alfred O. Hero, and Yang Chen. Solar flare intensity prediction with machine learning models. *Space Weather*, 18, 2019.
[13] Peter Kairouz, H. B. McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary B. Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Salim Y. El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascon, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zäïd Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konecný, Aleksandra Korolova, Farinaz Koushanfar, Oluwasanmi Koyejo, Tancrede Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, R. Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Xiaodong Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao. Advances and open problems in federated learning. Found. Trends Mach. Learn., 14:1-210, 2019.
[14] Alan F. Karr, Xiaodong Lin, Ashish P. Sanil, and Jerome P. Reiter. Privacy-preserving analysis of vertically partitioned data using secure matrix products. Journal of Official Statistics, 25: 125-138, 2009.
[15] Gayaneh Kyureghian, Oral Capps, and Rodolfo M. Nayga. A missing variable imputation methodology with an empirical application. 2011.