FTML 2021

6 papers

Advances and Open Problems in Federated Learning 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
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Dynamical Variational Autoencoders: A Comprehensive Review Laurent Girin, Simon Leglaive, Xiaoyu Bie, Julien Diard, Thomas Hueber, Xavier Alameda-Pineda
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Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT Sean B. Holden
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Minimum-Distortion Embedding Akshay Agrawal, Alnur Ali, Stephen P. Boyd
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Spectral Methods for Data Science: A Statistical Perspective Yuxin Chen, Yuejie Chi, Jianqing Fan, Cong Ma
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Tensor Regression Jiani Liu, Ce Zhu, Zhen Long, Yipeng Liu
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