Large Scale Distributed Sparse Precision Estimation

By Huahua Wang, Arindam Banerjee, Cho-Jui Hsieh, Pradeep Ravikumar, Inderjit S. Dhillon. 2013

39 citations recorded by the source corpus.

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. Ameet Talwalkar
3. Maziar Sanjabi
4. Tian Li
5. Chao-Kai Chiang
6. Pan Zhou
7. Chencheng Li
8. André Ricardo Gonçalves

Citations in the corpus

• Federated Multi-Task Learning (2017, background): “This is a well-studied problem, and many scalable algorithms have been proposed to solve it [50, 15, 18].”
• Differentially Private Distributed Online Learning (2018, methodology): “So far, there have been two classical effective methods for sparse online learning [13], [14], [15].”
• A General Analysis of the Convergence of ADMM (2015, unknown): “We refer to ρ > 0 as the step-size parameter.”
• Multi-task Sparse Structure Learning with Gaussian Copula Models (2016, methodology): “In structure learning, we estimate the (conditional) dependence structures between random variables in a high-dimensional distribution, and major advances have been achieved in the past few years (Banerjee et al., 2008; Friedman et al., 2008; Cai et al., 2011; Wang et al., 2013).”