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High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion

6 July 2011

Anima Anandkumar

Vincent Y. F. Tan

A. Willsky

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Papers citing "High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion"

42 / 42 papers shown

Title
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps Jonathan A. Kelner Frederic Koehler Raghu Meka Dhruv Rohatgi 52 2 0 23 Feb 2024
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach Sérgio Machado Anirudh Sridhar P. Gil J. Henriques J. M. F. Moura A. Santos CML 42 2 0 08 Aug 2022
Learning Continuous Exponential Families Beyond Gaussian C. Ren Sidhant Misra Marc Vuffray A. Lokhov 70 5 0 18 Feb 2021
Algorithms for Learning Graphs in Financial Markets José Vinícius de Miranda Cardoso Jiaxi Ying Daniel P. Palomar CML AIFin 117 17 0 31 Dec 2020
Learning Undirected Graphs in Financial Markets J. Cardoso Daniel P. Palomar AIFin 60 26 0 20 May 2020
Learning Gaussian Graphical Models via Multiplicative Weights Anamay Chaturvedi Jonathan Scarlett 49 3 0 20 Feb 2020
Graph Learning Under Partial Observability Vincenzo Matta A. Santos Ali H. Sayed 107 0 0 18 Dec 2019
Structured Graph Learning Via Laplacian Spectral Constraints Sandeep Kumar Jiaxi Ying J. Cardoso Daniel P. Palomar 115 58 0 24 Sep 2019
Topology Inference over Networks with Nonlinear Coupling A. Santos Vincenzo Matta Ali H. Sayed 115 0 0 21 Jun 2019
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds Jonathan A. Kelner Frederic Koehler Raghu Meka Ankur Moitra 134 32 0 03 May 2019
Total positivity in exponential families with application to binary variables Steffen Lauritzen Caroline Uhler Piotr Zwiernik 158 13 0 01 May 2019
A Unified Framework for Structured Graph Learning via Spectral Constraints Sandeep Kumar Jiaxi Ying José Vinícius de Miranda Cardoso Daniel P. Palomar 86 115 0 22 Apr 2019
Graph Learning over Partially Observed Diffusion Networks: Role of Degree Concentration Vincenzo Matta A. Santos Ali H. Sayed 135 2 0 05 Apr 2019
Structure Learning of Sparse GGMs over Multiple Access Networks Mostafa Tavassolipour Armin Karamzade Reza Mirzaeifard S. Motahari M. M. Manzuri Shalmani 138 2 0 26 Dec 2018
Testing Changes in Communities for the Stochastic Block Model Aditya Gangrade Praveen Venkatesh B. Nazer Venkatesh Saligrama 59 3 0 29 Nov 2018
Identifiability in Gaussian Graphical Models D. Soh S. Tatikonda 76 3 0 10 Jun 2018
Stationary Geometric Graphical Model Selection I. Soloveychik Vahid Tarokh 134 0 0 10 Jun 2018
Local Tomography of Large Networks under the Low-Observability Regime A. Santos Vincenzo Matta Ali H. Sayed 48 27 0 23 May 2018
Region Detection in Markov Random Fields: Gaussian Case I. Soloveychik Vahid Tarokh 133 2 0 12 Feb 2018
Lower Bounds for Two-Sample Structural Change Detection in Ising and Gaussian Models Aditya Gangrade B. Nazer Venkatesh Saligrama 66 6 0 28 Oct 2017
Learning Graphs with Monotone Topology Properties and Multiple Connected Components Eduardo Pavez Hilmi E. Egilmez Antonio Ortega 87 55 0 31 May 2017
Information Theoretic Optimal Learning of Gaussian Graphical Models Sidhant Misra Marc Vuffray A. Lokhov 41 5 0 15 Mar 2017
An Efficient Pseudo-likelihood Method for Sparse Binary Pairwise Markov Network Estimation Sinong Geng Zhaobin Kuang David Page 141 11 0 27 Feb 2017
Maximum likelihood estimation in Gaussian models under total positivity Steffen Lauritzen Caroline Uhler Piotr Zwiernik 138 70 0 14 Feb 2017
Mixing Times and Structural Inference for Bernoulli Autoregressive Processes Dimitrios Katselis Carolyn L. Beck R. Srikant 46 15 0 19 Dec 2016
Lower Bounds on Active Learning for Graphical Model Selection Jonathan Scarlett Volkan Cevher 58 8 0 08 Jul 2016
On the Difficulty of Selecting Ising Models with Approximate Recovery Jonathan Scarlett Volkan Cevher 78 12 0 11 Feb 2016
Active Learning Algorithms for Graphical Model Selection Gautam Dasarathy Aarti Singh Maria-Florina Balcan Jonghyuk Park 163 25 0 01 Feb 2016
Information-theoretic limits of Bayesian network structure learning Asish Ghoshal Jean Honorio 162 25 0 27 Jan 2016
New Optimisation Methods for Machine Learning Aaron Defazio 77 6 0 09 Oct 2015
Efficient Neighborhood Selection for Gaussian Graphical Models Yingxiang Yang Jalal Etesami Negar Kiyavash TPM 36 1 0 22 Sep 2015
High-dimensional consistency in score-based and hybrid structure learning Preetam Nandy Alain Hauser Marloes H. Maathuis 106 130 0 09 Jul 2015
On model misspecification and KL separation for Gaussian graphical models Varun Jog Po-Ling Loh 82 15 0 10 Jan 2015
Estimation of positive definite M-matrices and structure learning for attractive Gaussian Markov Random fields M. Slawski Matthias Hein 162 104 0 26 Apr 2014
Active Learning for Undirected Graphical Model Selection Divyanshu Vats Robert D. Nowak Richard G. Baraniuk 151 8 0 13 Apr 2014
Statistical Structure Learning, Towards a Robust Smart Grid Hanie Sedghi E. Jonckheere 426 4 0 07 Mar 2014
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Noisy Matrix Decomposition Hanie Sedghi Anima Anandkumar E. Jonckheere 113 13 0 20 Feb 2014
Concave Penalized Estimation of Sparse Gaussian Bayesian Networks Bryon Aragam Qing Zhou CML 132 107 0 04 Jan 2014
A Junction Tree Framework for Undirected Graphical Model Selection Divyanshu Vats Robert D. Nowak CML 126 8 0 17 Apr 2013
Pairwise MRF Calibration by Perturbation of the Bethe Reference Point Cyril Furtlehner Yufei Han Jean-Marc Lasgouttes Victorin Martin 178 5 0 19 Oct 2012
Learning loopy graphical models with latent variables: Efficient methods and guarantees Anima Anandkumar R. Valluvan 193 50 0 17 Mar 2012
High-dimensional structure estimation in Ising models: Local separation criterion Anima Anandkumar Vincent Y. F. Tan Furong Huang A. Willsky 221 114 0 08 Jul 2011