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We consider the task of learning Ising models when the signs of different random variables are flipped independently with possibly unequal, unknown probabilities.
Beitrag zur theorie des ferromagnetismus
E. Ising · 1925
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Approximating discrete probability distributions with dependence trees
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The ising model on trees: Boundary conditions and mixing time
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Fields of experts: A framework for learning image priors
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Weak pairwise correlations imply strongly correlated network states in a neural population
E. Schneidman, M. J. Berry II, R. Segev, and W. Bialek · 2006
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Efficient structure learning of markov networks using l _ 1 l\_1 -regularization
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Self-organizing ising model of financial markets
W.-X. Zhou and D. Sornette · 2007
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Reconstruction of markov random fields from samples: Some observations and algorithms
G. Bresler, E. Mossel, and A. Sly · 2008
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High-dimensional ising model selection using l1-regularized logistic regression
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High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity
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Learning graphical models using multiplicative weights
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Optimal structure and parameter learning of ising models
A. Y. Lokhov, M. Vuffray, S. Misra, and M. Chertkov · 2018
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Detecting influence campaigns in social networks using the ising model
N. G. d. Mesnards and T. Zaman · 2018
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Sparse logistic regression learns all discrete pairwise graphical models
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Structure learning of antiferromagnetic ising models
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S. Wu, S. Sanghavi, and A. G. Dimakis · 2018
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Testing ising models
C. Daskalakis, N. Dikkala, and G. Kamath · 2019
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Learning ising models with independent failures
S. Goel, D. M. Kane, and A. R. Klivans · 2019
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Non-parametric structure learning on hidden tree-shaped distributions
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