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We address the problem of learning of continuous exponential family distributions with unbounded support.
On distributions admitting a sufficient statistic
Bernard Osgood Koopman · 1936
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Model-based gaussian and non-gaussian clustering
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Andreas Wächter and Lorenz T Biegler · 2006
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First-order methods for sparse covariance selection
Alexandre d’Aspremont, Onureena Banerjee, and Laurent El Ghaoui · 2008
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Graphical models, exponential families, and variational inference
Martin J Wainwright, Michael I Jordan, et al · 2008
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The nonparanormal: Semiparametric estimation of high dimensional undirected graphs
Han Liu, John Lafferty, and Larry Wasserman · 2009
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Model selection in gaussian graphical models: High-dimensional consistency of ℓ 1 \ell_{1} -regularized MLE
Pradeep Ravikumar, Garvesh Raskutti, Martin J Wainwright, and Bin Yu · 2009
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Copula gaussian graphical models and their application to modeling functional disability data
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Aritra Sengupta, Noel Cressie, Brian H Kahn, and Richard Frey · 2016
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Interaction screening: Efficient and sample-optimal learning of Ising models
Marc Vuffray, Sidhant Misra, Andrey Lokhov, and Michael Chertkov · 2016
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Learning additive exponential family graphical models via \ \backslash ell_ { \{ 2, 1 } \} -norm regularized m-estimation
Xiao-Tong Yuan, Ping Li, Tong Zhang, Qingshan Liu, and Guangcan Liu · 2016
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Jump: A modeling language for mathematical optimization
Iain Dunning, Joey Huchette, and Miles Lubin · 2017
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Zhanyu Ma · 2011
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High-dimensional gaussian graphical model selection: Walk summability and local separation criterion
Animashree Anandkumar, Vincent YF Tan, Furong Huang, and Alan S Willsky · 2012
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Sparse nonparametric graphical models
John Lafferty, Han Liu, Larry Wasserman, et al · 2012
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High-dimensional semiparametric gaussian copula graphical models
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Regularized rank-based estimation of high-dimensional nonparanormal graphical models
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Optimal structure and parameter learning of Ising models
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On semiparametric exponential family graphical models
Zhuoran Yang, Yang Ning, and Han Liu · 2018
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Learning ising models with independent failures
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Learning of discrete graphical models with neural networks
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Learning some popular gaussian graphical models without condition number bounds
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https://github.com/JuliaMath/QuadGK.jl , 2020
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Efficient learning of discrete graphical models
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