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Foundations and Trends in Machine Learning, Volume 14

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Volume 14, Numbers 1-2, 2021

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Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista A. Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D'Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaïd 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, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Hang Qi, Daniel Ramage, Ramesh Raskar, Mariana Raykova, Dawn 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, Sen Zhao:
Advances and Open Problems in Federated Learning. 1-210

Volume 14, Number 3, 2021

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Akshay Agrawal, Alnur Ali, Stephen P. Boyd:
Minimum-Distortion Embedding. 211-378

Volume 14, Number 4, 2021

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Jiani Liu, Ce Zhu, Zhen Long, Yipeng Liu:
Tensor Regression. 379-565

Volume 14, Number 5, 2021

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Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma:
Spectral Methods for Data Science: A Statistical Perspective. 566-806

Volume 14, Number 6, 2021

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Sean B. Holden:
Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT. 807-989

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