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Logarithm / Graph / Estimation theory / Tutte polynomial / Path decomposition / Graph theory / Mathematics / Theoretical computer science


On the Difficulty of Learning Power Law Graphical Models Rashish Tandon Pradeep Ravikumar
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Document Date: 2013-11-23 00:08:40


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File Size: 957,87 KB

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Company

Neural Information Processing Systems / Cambridge University Press / /

Currency

pence / /

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Facility

Computer Science University of Texas / /

IndustryTerm

power-law degree distribution / power-law exponent / scenarios e.g. internet graphs / power law graphs / internet topology / biological networks / learning power law graphical models / power law i.e. / power-law graph / greedy algorithm / scale-free evolving networks / power-law network analysis / power-law relationships / power-law models / power-law graphical model estimation / power law / social networks / power-law graphs / scale-free networks / scale free networks / power-law structured graphical model selection / power-law graphical models / power-law max / power-law graphical model selection / random networks / power-law graphical models i.e. / /

OperatingSystem

Xp / /

Organization

Cambridge University / University of Texas at Austin / National Science Foundation / American Mathematical Society / Rashish Tandon Pradeep Ravikumar Department / Computer Science University / Learning Power Law Graphical Models Rashish Tandon Pradeep Ravikumar Department / Department of Defense / /

Person

A. Anandkumar / V / Chung-Lu Model / /

Position

statistician / /

ProvinceOrState

South Dakota / Texas / /

PublishedMedium

Journal of Machine Learning Research / /

Technology

greedy algorithm / Machine Learning / /

URL

http /

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