D-FW: Communication efficient distributed algorithms for high-dimensional sparse optimization

By Jean Lafond, Hoi-To Wai, Eric Moulines. 2016

13 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Aurélien Bellet
2. Valentina Zantedeschi
3. Marc Tommasi
4. Wotao Yin
5. Jinshan Zeng
6. Gauri Joshi
7. Jianyu Wang
8. Mingyi Hong

In Computational Mechanics

1. Jinshan Zeng
2. Wotao Yin
3. Marc Tommasi
4. Aurélien Bellet
5. Valentina Zantedeschi
6. Yin Zhang
7. Ming Yan
8. Stanley J. Osher

In Computer Networks and Communications

1. Jinshan Zeng
2. Marc Tommasi
3. Aurélien Bellet
4. Wotao Yin
5. Valentina Zantedeschi
6. Ali H. Sayed
7. Aryan Mokhtari
8. Mingyi Hong

Citations in the corpus

• Fully Decentralized Joint Learning of Personalized Models and Collaboration Graphs (2019, background): “In most existing work, the goal is to learn the same global model for all users by minimizing the average of the local objectives (Duchi et al., 2012; Wei and Ozdaglar, 2012; Colin et al., 2016; Lafond et al., 2016; Lian et al., 2017; Jiang et al., 2017; Tang et al., 2018; Lian et al., 2018).”
• On Nonconvex Decentralized Gradient Descent (2016, background): “Note that the bounded gradient assumption is a regular assumption in the convergence analysis of decentralized gradient methods (see, [4], [5], [18], [27], [35], [36], [56], [57], [62] for example), even in the convex setting [24] and also [10], though it is not required for centralized gradient des”