Hessian-CoCoA : a general parallel and distributed framework for non-strongly convex regularizers

By Matilde Gargiani. 2017

How researchers see this paper

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

1. Virginia Smith
2. Jakub Konecný
3. Martin Jaggi
4. Lie He
5. Peter Richtárik
6. Chenxin Ma
7. Michael I. Jordan
8. Martin Takác

Citations in the corpus

• COLA: Decentralized Linear Learning (2018, methodology): “Other centralized algorithm representatives are parallel SGD variants such as [Agarwal and Duchi, 2011, Zinkevich et al., 2010] and more recent distributed second-order methods [Zhang and Lin, 2015, Reddi et al., 2016, Gargiani, 2017, Lee and Chang, 2017, Dünner et al., 2018, Lee et al., 2018].”
• CoCoA: A General Framework for Communication-Efficient Distributed Optimization (2016, methodology): “, 2017), second-order algorithm extensions (Gargiani, 2017; Lee and Chang, 2017; Dünner et al., 2018; Lee et al., 2018), and accelerated methods (Ma et al.”
• A Distributed Second-Order Algorithm You Can Trust (2018, methodology): “In an earlier work by (Gargiani, 2017) a modification of Co-CoA was discussed which incorporates local second-order information for the general class of problems (1).”