Efficient learning of discrete graphical models*

DOI10.1088/1742-5468/ac3aeaOpenAlexW4206363690MaRDI QIDQ5032038

Andrey Y. Lokhov, Sidhant Misra, Marc Vuffray

Publication date: 16 February 2022

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1902.00600

zbMATH Keywords

learning theorymachine learningnetwork reconstruction

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
• Mirror descent and nonlinear projected subgradient methods for convex optimization.
• The Ordered Subsets Mirror Descent Optimization Method with Applications to Tomography
• Efficiently Learning Ising Models on Arbitrary Graphs
• Information-Theoretic Limits of Selecting Binary Graphical Models in High Dimensions
• Approximating discrete probability distributions with dependence trees
• Reconstruction of Markov Random Fields from Samples: Some Observations and Algorithms

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