Recovering the Graph Underlying Networked Dynamical Systems under
Partial Observability: A Deep Learning Approach

v1v2v3 (latest)

Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach

8 August 2022

Sérgio Machado

Anirudh Sridhar

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Papers citing "Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach"

14 / 14 papers shown

Title
An Unbiased Symmetric Matrix Estimator for Topology Inference under Partial Observability Yupeng Chen Zhiguo Wang Xiaojing Shen 41 5 0 29 Mar 2022
Local Tomography of Large Networks under the Low-Observability Regime A. Santos Vincenzo Matta Ali H. Sayed 46 27 0 23 May 2018
Generalized Network Dismantling Xiaolong Ren Niels Gleinig Dirk Helbing Nino Antulov-Fantulin 46 153 0 04 Jan 2018
SILVar: Single Index Latent Variable Models Jonathan Mei José M. F. Moura 81 24 0 09 May 2017
Signal Processing on Graphs: Causal Modeling of Unstructured Data Jonathan Mei José M. F. Moura CML AI4TS 64 191 0 28 Feb 2015
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components Philipp Geiger Kun Zhang Biwei Huang Dominik Janzing Bernhard Schölkopf CML LLMSV 51 79 0 14 Nov 2014
Hardness of parameter estimation in graphical models Guy Bresler D. Gamarnik Devavrat Shah 56 32 0 12 Sep 2014
Causal Inference in the Presence of Latent Variables and Selection Bias Peter Spirtes Christopher Meek Thomas S. Richardson CML 199 444 0 20 Feb 2013
Learning loopy graphical models with latent variables: Efficient methods and guarantees Anima Anandkumar R. Valluvan 145 50 0 17 Mar 2012
High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion Anima Anandkumar Vincent Y. F. Tan A. Willsky 108 90 0 06 Jul 2011
Learning the Dependence Graph of Time Series with Latent Factors A. Jalali Sujay Sanghavi CML 86 44 0 09 Jun 2011
Learning high-dimensional directed acyclic graphs with latent and selection variables Diego Colombo Marloes H. Maathuis M. Kalisch Thomas S. Richardson CML 126 466 0 29 Apr 2011
Learning Networks of Stochastic Differential Equations José Bento M. Ibrahimi Andrea Montanari 128 73 0 01 Nov 2010
Latent variable graphical model selection via convex optimization V. Chandrasekaran P. Parrilo A. Willsky CML 207 509 0 06 Aug 2010

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