2009
NIPS
NeurIPS 2009
Which graphical models are difficult to learn?
Abstract
We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-complexity algorithms systematically fail when the Markov random field develops long-range correlations. More precisely, this phenomenon appears to be related to the Ising model phase transition (although it does not coincide with it).
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The Questioner
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Topic Pioneer
— Graphical Models
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Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning
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Trend Setter
— Graphical Models
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Hot Topic Early Bird
— graphical model
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Optimization & Theory > Learning Theory
Machine Learning > Optimization & Theory > Theory
Mathematics & Optimization > Mathematics > Probability
Machine Learning > Core Methods > Graphical Models
Machine Learning > Bayesian & Probabilistic > Graphical Models
Machine Learning > Learning Types > Structure Learning