
Linear and Parallel Learning of Markov Random Fields
We introduce a new embarrassingly parallel parameter learning algorithm ...
08/29/2013 ∙ by Yariv Dror Mizrahi, et al. ∙ 0 ∙ shareread it

Markov random fields factorization with contextspecific independences
Markov random fields provide a compact representation of joint probabili...
06/10/2013 ∙ by Alejandro Edera, et al. ∙ 0 ∙ shareread it

Adversarial Variational Inference and Learning in Markov Random Fields
Markov random fields (MRFs) find applications in a variety of machine le...
01/24/2019 ∙ by Chongxuan Li, et al. ∙ 18 ∙ shareread it

On the tradeoff between complexity and correlation decay in structural learning algorithms
We consider the problem of learning the structure of Ising models (pairw...
10/08/2011 ∙ by José Bento, et al. ∙ 0 ∙ shareread it

Learning from Complex Systems: On the Roles of Entropy and Fisher Information in Pairwise Isotropic Gaussian Markov Random Fields
Markov Random Field models are powerful tools for the study of complex s...
08/25/2011 ∙ by Alexandre L. M. Levada, et al. ∙ 0 ∙ shareread it

Bayesian Structure Learning for Markov Random Fields with a Spike and Slab Prior
In recent years a number of methods have been developed for automaticall...
08/09/2014 ∙ by Yutian Chen, et al. ∙ 0 ∙ shareread it

HingeLoss Markov Random Fields and Probabilistic Soft Logic
A fundamental challenge in developing highimpact machine learning techn...
05/17/2015 ∙ by Stephen H. Bach, et al. ∙ 0 ∙ shareread it
MCMC Learning
The theory of learning under the uniform distribution is rich and deep, with connections to cryptography, computational complexity, and the analysis of boolean functions to name a few areas. This theory however is very limited due to the fact that the uniform distribution and the corresponding Fourier basis are rarely encountered as a statistical model. A family of distributions that vastly generalizes the uniform distribution on the Boolean cube is that of distributions represented by Markov Random Fields (MRF). Markov Random Fields are one of the main tools for modeling high dimensional data in many areas of statistics and machine learning. In this paper we initiate the investigation of extending central ideas, methods and algorithms from the theory of learning under the uniform distribution to the setup of learning concepts given examples from MRF distributions. In particular, our results establish a novel connection between properties of MCMC sampling of MRFs and learning under the MRF distribution.
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