A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation

10 July 2014

Papers citing "A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation"

11 / 11 papers shown

Title
Learning the hub graphical Lasso model with the structured sparsity via an efficient algorithm Chengjing Wang Peipei Tang Wen-Bin He Meixia Lin 33 0 0 17 Aug 2023
Learning Gaussian Graphical Models with Ordered Weighted L1 Regularization Cody Mazza-Anthony Bogdan Mazoure Mark J. Coates 22 3 0 06 Jun 2019
Learning the effect of latent variables in Gaussian Graphical models with unobserved variables Marina Vinyes G. Obozinski CML 11 2 0 20 Jul 2018
Learning Gaussian Graphical Models Using Discriminated Hub Graphical Lasso Z. Li Jingtian Bai Weilian Zhou 13 1 0 17 May 2017
Structure Learning in Graphical Modeling Mathias Drton Marloes H. Maathuis CML 39 246 0 07 Jun 2016
Network Inference by Learned Node-Specific Degree Prior Qingming Tang Lifu Tu Weiran Wang Jinbo Xu 13 0 0 07 Feb 2016
Log-Normal Matrix Completion for Large Scale Link Prediction Brian Mohtashemi T. Ketseoglou 25 1 0 28 Jan 2016
Learning Nonparametric Forest Graphical Models with Prior Information Yuancheng Zhu Zhe Liu S. Sun 9 2 0 12 Nov 2015
New Optimisation Methods for Machine Learning Aaron Defazio 41 6 0 09 Oct 2015
Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior Qingming Tang S. Sun Jinbo Xu 32 1 0 07 Mar 2015
Learning Graphical Models With Hubs Kean Ming Tan Palma London Karthika Mohan Su-In Lee Maryam Fazel Daniela Witten 38 98 0 28 Feb 2014